## Usage of an Anonymization Solution at Courts in the Canton of Aargau, Switzerland

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### Who is using it?
The High Court of the Canton of Aargau in Switzerland with overall roughly 470 employees of which 124 are using an anonymization solution.

### What problem(s) are they solving?
The court wanted to publish court decisions but needed to anonymize sensitive data before being able to do so. Therefore, they chose Balo.ai ([https://www.balo.ai](https://balo.ai/)) which allows for the anonymization of text documents while preserving the readability of the document and keeping all data on-premise. Through the usage of the anonymization solution, the court was able to publish a lot more decisions than before (1673 in one year as opposed to roughly 100 in the past).

### Which NLP technologies are they using?
Balo.ai works with an on-premise language model so that no data ever leaves the environment the tool is used in, which is particularly beneficial from a data protection perspective as the target users are mostly public institutions in the legal sector who operate under strict data protection regulations. The model is specifically trained for anonymization tasks and doesn't require a lot of computing resources, which makes it possible to host it on machines with limited resources. Additionally, there is a MS Word Add-In that makes anonymization suggestions that the user needs to manually accept and a separate tool for PDFs with the same functionality.

### Stage
Production - the courts are using the solution for anonymization

### Challenges
- The LLM is not able to understand all legal terminology and ideally needs to be fine-tuned with specific data for the customer
- Users would like a fully automated process which is not trivial
- Integration of AI-based tools into existing office software (e.g., through plugins) is not trivial as new versions require adaptations (also because many companies use no longer supported legacy software)

### Source
Two interviews conducted in April and May 2024, led by sebis researchers, one with a co-founder of Balo.ai and one with a test manager working for the courts of the Canton of Aargau.

Usage of an Anonymization Solution at Courts in the Canton of Aargau, Switzerland

## LLM Usage at a German Law Firm

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### Who is using it?
The legal tech department of a German law firm is developing and using an AI-based solution for their daily legal work. The department is responsible for legal operations and tries to model legal processes as a series of steps that can be automated with software solutions.

### What problem(s) are they solving?
The motivation for the solution was an ever increasing influx of legal correspondence that took too much time sorting through and properly linking it to the respective cases.
Using an AI-based solution to go over all incoming correspondence automatically improves their efficiency since documents are classified and information like deadlines is extracted automatically.

### Which NLP technologies are they using?
The law firm uses Salesforce as CRM where they integrated their AI solution through multiple complex workflows. The solution Blaubach.ai ([https://www.blaubach.ai/](https://www.blaubach.ai/)) was specifically developed for internal use first but is now also offered to other interested parties and can be adapted to their specific needs.
The general workflow is that incoming data is passed through the AI solution that automatically detects the docket number and extracts relevant information from the document assigning it to the correct case.
The lawyers annotated the training data themselves and handed the labels off to the data scientists in the team to adapt the AI.

### Stage
Production - users are using the tool in their daily workflow

### Challenges
- The tool occassionally makes mistakes that need to be double-checked manually by humans
- Integrating new solutions into the existing legal IT landscape and adapting it to existing APIs is not trivial

### Source
Two interviews conducted in April 2024, led by sebis researchers, one interviewee is a lawyer and the head of the legal tech department at the company, the other is a data scientist.

LLM Usage at a German Law Firm

## LLM Usage at a German Law Firm

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### Who is using it?
The legal tech department of a German law firm with over 350 employees is exploring the usage of AI-based solutions to support the daily legal work of the lawyers. The department consists of 6 employees and is responsible for the digital transformation and legal tech strategy.

### What problem(s) are they solving?
The most important problem to solve is how to schematically process large collections of documents. Therefore, extraction of relevant information from documents is the main use case the department is using AI-based solutions for. Other related use cases are the classification of documents and being able to ask the AI questions about the contents of a document.

### Which NLP technologies are they using?
The law firm has been using tools based on machine learning for multiple years and is currently looking at solutions like Legisway Analyzer ([https://www.wolterskluwer.com/en-gb/solutions/legisway/legisway-analyzer](https://www.wolterskluwer.com/en-gb/solutions/legisway/legisway-analyzer)) and Harvey ([https://www.harvey.ai/](https://www.harvey.ai/)). The output is perceived to be really good but still requires manual checking to make sure the information is not hallucinated.

### Stage
Production - users are using the tool in their daily workflow

### Challenges
- Most processes can not be fully automated yet and still require a human in the loop
- OCR does not always work as intended, especially for old documents that only exist on paper and need to be scanned
- Form-based data is more difficult to process with NLP than unstructured textual data (full sentences)
- Ensuring GDPR compliance

### Source
Two interviews conducted in May 2024, led by sebis researchers, one interviewee is a lawyer and the head of the legal tech department at the company, the other is an associate of the same law firm and part of the legal tech team.

## LLM Usage at a German Law Firm

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### Who is using it?
A German law firm with roughly 180 employees has established a small legal tech lab and is exploring the usage of AI-based solutions to support their daily legal work. The department consists of 6 employees and is responsible for the digital transformation and legal tech strategy.

### What problem(s) are they solving?
The employees are trying to improve their knowledge management. The main goal is to make all relevant documents and data accessible through semantic search so that lawyers can easily find what they are looking for and then query these documents and receive appropriate responses from the AI. One example is to upload a document and ask for a summary of the decision. If the answer is not specific enough, the lawyer will ask a follow-up question to clear up any uncertainties. The users perceived big improvements in their efficiency as the solution is able to assist them in finding relevant information quicker than when looking for it themselves, especially considering that some cases consist of many documents which in turn consist of many pages. As LLMs may hallucinate responses, all users are urged to double check the generated responses.

### Which NLP technologies are they using?
The law firm is currently integrating Prime Legal AI ([https://primelegal.de/en/](https://primelegal.de/en/)) into their workflow. Depending on the use case, the AI solution utilizes multiple already existing LLMs (e.g., GPT-4, GPT-4-Turbo, Claude-3-Opus) under the hood to provide its various services. The users can select from a library of pre-defined prompts (or write their own) and the data they want them used upon. The anonymization functionality works on-premise whereas the rest of the functionality is available through the cloud. The service is accessible through a web application but also through an API. An entity-relationship model was used to train knowledge graphs which are the foundation for Prime Legal AI.

### Stage
Production - users are using the tool in their daily workflow

### Challenges
- Introduction of new tools requires training and marketing for the potential new users and ideally an IT department that takes care of the integration
- Data needs to be anonymized first before it can be fed into the tool
- Usage of AI leads to a paradigm shift from billing by the hour to a flat fee model

### Source
Two interviews conducted in May 2024, led by sebis researchers, one interviewee is the CEO of Prime Legal AI and the other is a lawyer specializing in corporate law and responsible for legal tech at their law firm.

## AI Usage at a German Law Firm

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### Who is using it?

A large law firm with over 100 lawyers and accountants organized in teams of 10-12 employees distributed over 3 locations is in the process of evaluating legal AI usage at their firm. The target audience of the tools are the lawyers.
As of the date of the interview, the law firm has not yet adopted any company-wide legal AI solution but is testing various tools and models in a decentralized way to determine in which cases an adoption is feasible. Some lawyers already use personal ChatGPT accounts to assist them in their daily tasks.

### What problem(s) are they trying to solve?

The employees are in the phase of testing and figuring out what problems related to the legal domain can feasibly be solved with the usage of LLM-based tools.
Some lawyers already use personal ChatGPT accounts as a personal assistant to assist them in their daily tasks which are not restricted to the legal domain such as creating and summarizing texts (e.g., emails) or helping with translations, etc.
Regarding the legal use cases, due to the decentralized nature of the law firm the respective tasks of each department are diverse.
The use cases under investigation include but are not limited to:
- Document generation
- Document analysis of large(r) collections of documents
- Document summarization
- Information research and retrieval

### Which NLP technologies are they using?

The law firm is looking at various tools on the market, especially ones also being used by other German law firms, to get an overview of the current tool landscape. The solutions under investigation mainly operate with OpenAI and LLaMa models and are either deployed in the cloud or on-premise. In general, the law firm would prefer an on-premise solution to maintain control over the service. The language requirements are German and English. There is no intention of building their own tools but rather wait for the establishment of market standards that can then be adopted into their own IT landscape.

### How do they test the quality of the tools?

Since the rise of LLMs there have been multiple individual uncoordinated tests of LLM usage on a personal level but in the past few months an effort has been made to coordinate the testing and conduct it in a more organized way.
For this purpose, the various departments of the law firm individually test tools regarding their feasibility for their respective use cases. The employees do not use any specific evaluation metrics but come together and define success criteria and test scenarios for each task and then later on get together in working groups to discuss the findings.

### How is data privacy cared for?

No privacy-sensitive data is used for testing to avoid any privacy-related issues.

### Stage

Research - different users are experimenting with the usage of AI

### Challenges

- Commercial / Effort - For a revenue-driven firm the willingness to expend resources on adopting a new solution is limited as it requires additional effort for data preparation and testing before a solution can be integrated into the existing IT landscape.
- Privacy issues - Data fed into the model should not be used for training. For older data (historic data) there are no privacy agreements that allow the usage of the data in such tools.
- Compliance with AI Act

### Source
Interview conducted in February 2024, led by sebis researchers. The interviewees are working at the law firm in the position of lawyer and Digital Transformation Manager respectively.

AI Usage at a German Law Firm

## LLM Usage at a German Accounting Firm

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### Who is using it?
The employees of a large German accounting firm with over 15.000 employees are exploring the usage of LLM-based tools to improve their efficiency.

### What problem(s) are they solving?
The employees use an LLM-based tool for document analysis, contract drafting, negotation assistance, and legal research. The most important use case for the users is document summarization, e.g., of contracts.

### Which NLP technologies are they using?
The employees are using Leah offered by ContractPodAI ([https://contractpodai.com/leah/](https://contractpodai.com/leah/)). ContractPodAI provides a contract lifecycle management suite and Leah is a standalone SaaS solution that enables the usage of generative AI without having to implement the full CLM solution. Leah provides various functionality out-of-the-box and utilizes multiple LLMs under the hood to generate its responses but it can also be fine-tuned to the needs of the customers.

### Stage
Production - users are using the tool in their daily workflow

### Challenges
- Continuous change management and data migration is required to keep up with new requirements
- Integration of the tool into the existing IT landscape
- Many lawyers are not very tech-savvy and require training to use the tool effectively

### Source
Interview conducted in May 2024, led by sebis researchers. The interviewee is the head of legal tech at the company.

LLM Usage at a German Accounting Firm

## LLM Usage at a German Law Firm

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### Who is using it?
The corporate Merger & Acquistions department of a law firm totaling over 100 employees and specializing on German law is testing the usage of AI and LLMs to speed up their work.

### What problem(s) are they solving?
The employees use an LLM-based tool in hopes of improving their efficiency to draft contracts. Normally, new contracts are constructed by reusing and adapting clauses from past contracts. The tool helps automate this task by importing clauses from other documents and adapting them to the new context. The tool is used for assistance as it still requires a good amount of manual effort.
One reason for incorporating AI technology into their weekly routine is the perception that customers now expect it in this day and age.

### Which NLP technologies are they using?
A few selected lawyers of the department are testing Henchman ([https://henchman.io/](https://henchman.io/)), a multi-LLM contract drafting and negotiating AI solution, to assess a potential department-wide adoption of the tool. One of the employed LLMs is ChatGPT running on an Azure instance so that the data is not used to train the model and remains within the EU. Users can interact with the system through an Add-In in Word or Outlook.

### Stage
Research - different users are experimenting with the usage of LLMs

### Challenges
- Lawyers need to invest time and effort to incorporate the tool into their routine before any efficiency gains can be realized
- The tool is only as good as the existing documents from the past. Changes in legal regulations are naturally not reflected yet for future contracts.

### Source
Two interviews conducted in April and May 2024, led by sebis researchers. One interviewee is a lawyer working in corporate Mergers and Acquisitions at a German law firm and the other is a legal tech consultant.

## LLM Usage at a German Technology and Engineering Company

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### Who is using it?
The patent department of a global technology and engineering company, which includes 26 direct employees and around 200 in total, is testing the usage of AI and LLMs to speed up their work.

### What problem(s) are they solving?
The employees use the LLM for various tasks surrounding text generation and analysis. Since September 2023, they are testing what is possible concerning patents including patent drafting, mapping of patents, and patent analysis, i.e., retrieving key aspects out of the documents. The results are perceived to be quite promising but one still needs to invest some time refining them as they are not good enough to just be copy pasted and used as is.

The initial expectation was that you can just give the LLM a job and wait for it to come back with the results. However, one key learning is that you need to teach the model what you want, i.e., give it a persona and break down the instructions into very small parts.

Further use cases revolve around assistance in their daily jobs such as drafting emails or parts of reports and translations.

In the future, they would like to expand their usage of LLMs by leveraging the voice input capabilities. Ideally, the LLM will act as a personal assistant helping with their daily tasks, e.g., answering questions, providing explanations, finding and opening files, etc.

### Which NLP technologies are they using?
The employees have access to ChatGPT 3.5 with 128k tokens through the company's Azure instance which provides a certain level of security and confidentiality.
The department is considering the costs and benefits of prompt optimization or the potential fine-tuning or even training of own models.

### Stage
Research - different users are experimenting with the usage of LLMs

### Challenges
- Context window insufficient - 128k tokens are not enough for working with large documents
- Domain-specific knowledge required - LLMs are not specialized enough for such a technical domain especially when it comes to novel technology that is not part of training data
- Fine-tuning / Training models requires necessary know-how and good training data

### Source
Interview conducted in June 2024, led by sebis researchers. The interviewee is a lawyer and head of the patent department at the abovementioned company.

LLM Usage at a German Technology and Engineering Company

## LLM Usage at a German Healthcare Solution Provider

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### Who is using it?

A team of several in-house legal experts from a multi-national, German-based technical corporation specializing in healthcare solutions and services. 300 users are currently testing the usage of MS Copilot.

### What problem(s) are they solving?

The employees use LLM-based tools for various tasks in their daily work life. For one as a personal assistant for various tasks including quick general questions, translations, summaries of text, and searching for specific files on the system. Another task can be summed up under the umbrella term of content generation, e.g. for email drafts, marketing texts, etc.

The rise in popularity of ChatGPT led legal experts at the company to evaluate if they could also harness the power of LLMs to assist in the daily, routine work of legal contract generation and analysis, which typically requires a great deal of manual time and human effort. The usage of LLMs can help highlight important parts of contracts and give guidance on where a human expert should have a closer look. The contracts are based primarily on German law. Other legal tasks are also being considered.

Another emerging use case is the usage of LLMs to assist in software development wherefore it's currently being tested regarding legal regulations.

Finally, the organization is thinking about deploying chatbots to assist in the communication with customers which could result in a reduction of costs.

### Which NLP technologies are they using?

As a technical-oriented organization, employees already had existing access to the Microsoft Office products, which facilitated for the convenient experimentation with MS Copilot and ChatGPT on the Microsoft Azure Cloud. This cloud solution was chosen due to its ease of use, as well as the perceived privacy of using ChatGPT in the cloud, with no need for further training or fine-tuning on proprietary data. Prompt engineering is employed to optimize results.

Software engineers are also evaluating open-source models for generating code and documenting it automatically.

### How do they test the quality of their service?
The organization started using the ChatGPT playground in summer 2022. Before that, interested parties experimented with their personal ChatGPT accounts and non-sensitive data, subject to guidelines provided by the organization. The employees do not employ any prompt engineering guides on department level or use any evaluation metrics to determine the effectiveness of the model. The experts manually test the models for their various tasks to determine if their usage for the respective use cases makes sense. One finding is that the more the respective field is legally regulated, the less sense it makes to employ AI, e.g. German lease contracts are too strictly regulated so that LLMs struggle with producing accurate and correct results.

### How is data privacy cared for?
Documents are anonymized manually before uploading them to the cloud and making them accessible to the model.
Usage of the public ChatGPT interface is only allowed for non-privacy sensitive matters. For everything else the ChatGPT playground needs to be used as Microsoft claims to not use that data for training purposes.

### Stage
Research - different users are experimenting with the usage of LLMs

### Barriers to Adoption
- Hallucinations -– Quality, reliability, and correctness of generated responses varies depending on use case.
- Copyright infringement and ensuing liability risk - It's not always clear whether generated responses from the model contain copyrighted content.
- Privacy issues - Data fed into the model should not be used for training.


### Source
Interview conducted in January 2024, led by Prof. Florian Matthes and Nektarios Machner. The interviewee is a lawyer and responsible for the IT department at the abovementioned company, where he also looks into what is going on in the IT world regarding useful tools.


LLM Usage at a German Healthcare Solution Provider

## AI Solution Provider: Elephant Labs

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### Who is using it?
The solution is targeted at public institutions like the government, courts, and other regulatory offices. The company is currently in contact with the justice ministry of Bavaria to explore how the solution can provide value to them.

### What problem(s) are they solving?
The company Elephant Labs ([https://www.elephantlabs.ai/](https://www.elephantlabs.ai/)) was founded in December 2022 in an attempt to develop a solution that increases the efficiency of lawyers through the use of computer science. For example, court verdicts need to be published to the public but the regulations as to how and when are not clear. In order to publish such data, however, sensitive information needs to be masked first and this is what VerdictLLM was initially developed for.

### Which NLP technologies are they using?
The tool VerdictLLM utilizes the power of open-source LLMs and statistical methods to anonymize sensitive data. Additionally, the company uses embeddings to classify which information needs to be anonymized. The LLM is fine-tuned through synthetic data specifically optimized for applicability in the legal context.

### Challenges
- Establishing trust in LLM-based solutions with potential customers who primarily work with sensitive data
- Public institutions are reluctant to change existing processes (independent of AI)

### Source
Interview conducted in April 2024, led by sebis researchers. The interviewee is the founder of the company.

AI Solution Provider: Elephant Labs

## AI Solution Provider: fastlaw

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### Who is using it?
The solution is targeted at law firms and notaries working with land registration data.

### What problem(s) are they solving?
The company fastlaw ([https://fastlaw.online/](https://fastlaw.online/)) was founded as a spin-off from a law firm that has been working completely digitally for the past ten years alreay. Their AI solution is designed to read land register data and extract relevant information.

### Which NLP technologies are they using?
The company provides a CRM solution for law firms and an AI-based tool called GREGOR available as a SaaS solution that is able to read land register data through OCR and then extract relevant information which in turn can be exported again in a structured format. To achieve this, the company together with an undisclosed partner trained their own language model.

### Challenges
- Adapting the AI to correctly identify the relevant text passages
- Land register data is not standardized and can consist of multiple document excerpts assembled into one single document

### Source
Interview conducted in April 2024, led by sebis researchers. The interviewee is the head of legal engineering and business development of the company.

AI Solution Provider: fastlaw

## LLM Usage at a German Law Firm

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### Who is using it?
A large German law firm is developing their own solution to assist in their legal work but the solution is also provided to interested legal professionals who need to generate documents in their daily work, e.g., contracts.

### What problem(s) are they solving?
The main use case aims at generating legal documents based on templates and data inputs but the solution is also able to assist in other legal tasks surrounding document management. The company is currently evaluating the usage of AI in various aspects of their work to derive new use cases that can benefit from its usage.

### Which NLP technologies are they using?
The company uses AI pipelines (i.e., Retrieval Augmented Generation) that are independent of a specific LLM and always aim to use the best LLM depending on use case. Currently, they are working with GPT-4 deployed on their own Azure instance but are aiming at also including Claude Opus. The company took all their documents, anonymized them, and embedded them in a vector database to allow for a semantic search. From this existing knowledge base new documents can be generated through pre-defined prompts. The functionality is accessible through a Word Add-In that is connected to the pipeline built on the deepset cloud. Subtasks of the pipeline also employ other NLP methods such as Named Entity Recognition (NER).


### Stage
Research - the solution is under active development and different users are evaluating its usage

### Challenges
- Developing your own AI is resource-intensive and not practical in an environment with frequent law changes
- Users require proper training in order to utilize the full potential of novel AI-based solutions

### Source
Interview conducted in April 2024, led by sebis researchers. The interviewee is the CEO of a spin-off company.

## AI Solution Provider: Herlock Insights

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### Who is using it?
The solution is targeted at law firms and legal professionals.

### What problem(s) are they solving?
The main focus is to provide transparency regarding legal case data, i.e., every legal case is self-contained and builds the foundation that the software solution operates upon. Users can query the documents for information and ask about information related to each case.

### Which NLP technologies are they using?
The company provides an AI-based case analysis tool called Herlock.ai ([https://www.herlock.ai/](https://www.herlock.ai/)) that uses natural language processing (NLP) and vector embeddings to analyze legal case data. The solution is provided as SaaS deployed on a private cloud running on GCP or AWS. One of the used language models is ChatGPT but the company is looking at alternatives that can be deployed on-premise for customers that have this requirement. The solution organizes relevant information around individual legal cases and provides a visual timeline of chronologically sorted events next to allowing to search and compare documents and ask questions about them.

### Challenges
- Hallucinations can occur but are mitigated by providing references to the sources of the information
- Legal landspace is very diverse and many users are still uncertain about employing AI in their workflow

### Source
Interview conducted in April 2024, led by sebis researchers. The interviewee is the co-founder and CTO of the company.

AI Solution Provider: Herlock Insights

## AI Solution Provider: Hyde

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### Who is using it?
The solution is targeted at law firms and legal professionals.

### What problem(s) are they solving?
The motivation to focus on the legal sector stems from the fact that legal professionals primarily work with text which is predestined to being used with large language models. The offered AI suite aims to assist with multiple legal use cases including case management, document analysis, and document generation based on custom templates specific to the customer.

### Which NLP technologies are they using?
Hyde ([https://www.hyde.to/](https://www.hyde.to/)) is an AI suite based on language models. The company transitioned from building one large language model into building multiple smaller models specific for individual use cases such as semantic search to query documents for information. The solution is offered as SaaS in the cloud or on-premise depending on the customer's need.

### Challenges
- Customer acquisition is not easy in the legal domain as law firms are focused on their work and tend to not have much time for sales calls

### Source
Interview conducted in May 2024, led by sebis researchers. The interviewee is a late co-founder and the CPO at the company.

AI Solution Provider: Hyde

## AI Solution Provider: Justin Legal

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### Who is using it?
The solution is targeted at law firms and legal professionals.

### What problem(s) are they solving?
The company focuses on data collection and data quality to enable digitalization. Clients of law firms can use the solution to provide relevant data for their case in a structured form by following the steps in the tool. Lawyers are then able to review the data digitally and ask questions about the document to the AI assistant.

### Which NLP technologies are they using?
Justin Legal ([https://justin-legal.com/](https://justin-legal.com/)) provides their service through a SaaS web application. The underlying LLM is ChatGPT 4 deployed on an Azure instance which is used to provide features like text summarization and question & answering. The data can be used to generate new documents, export in various formats, or directly passed via API to standard software solutions.

### Challenges
- Properly modeling legal real-world scenarios in a digital system
- GDPR Compliance

### Source
Interview conducted in May 2024, led by sebis researchers. The interviewee is the CEO at the company.

AI Solution Provider: Justin Legal

## AI Solution Provider: Libra Tech

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### Who is using it?
The solution is targeted at law firms and legal professionals.

### What problem(s) are they solving?
Libra ([https://libratech.ai/](https://libratech.ai/)) wants to become an AI-powered law firm assistance provider to increase the efficiency of lawyers. For example, the tool is able to classify and extract information from documents and generate new documents like contracts.

### Which NLP technologies are they using?
The company is currently building modules in an AI pipeline to automate different tasks in a lawyer's workflow. They started out with using commercial APIs like ChatGPT but then proceeded to fine-tune their own models that will be used exclusively in the future. The solution is under active development and the company is building their own evaluation data sets while simultaneously receiving feedback from customers to evaluate their application.

### Challenges
- Compliance requirements
- Acquiring high-quality training data to fine-tune models to the legal domain
- Aligning the technical side with the business side

### Source
Interview conducted in April 2024, led by sebis researchers. The interviewee is a co-founder and the CTO of the company.

AI Solution Provider: Libra Tech

## AI Solution Provider: Mioto Labs

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### Who is using it?
The solution is targeted at small- to medium-sized law firms.

### What problem(s) are they solving?
Mioto Labs ([https://mioto.app/](https://mioto.app/)) offers custom-tailored solutions for customers in the legal domain. Typical use cases include but are not limited to contract generation based on pre-defined templates, document review and analysis, and information extraction.

### Which NLP technologies are they using?
The solution currently consists of a workflow builder for process automation. Commercial LLMs like ChatGPT through an Azure instance are used to assist in specific steps of the workflow, e.g., to extract information from documents. Depending on the needs of the customers, the team decided where and when in the workflow chain it makes sense to utilize LLMs to achieve a specific subtask.

### Challenges
- SaaS is difficult to market to customers in the legal domain
- Building custom solutions for customers requires a lot of flexibility from the development team as requirements are not often clear at the beginning

### Source
Interview conducted in May 2024, led by sebis researchers. The interviewee is a co-founder and the CTO of the company.

AI Solution Provider: Mioto Labs

## AI Solution Provider: Neur.on AI

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### Who is using it?
The solution is targeted at professionals from the legal and financial sector.

### What problem(s) are they solving?
The company Neur.on AI ([https://neur-on.ai/](https://neur-on.ai/)) specializes in translations and translation management.

### Which NLP technologies are they using?
The company currently provides multiple separate solutions like Lex Machina and Corrext that they eventually want to integrate into one overarching software solution. The solution is offered as a collaborate SaaS solution accessible through a web application. The system preprocesses data with the help of multiple LLMs, e.g., to categorize what kind of law documents pertain to, and then the system can provide machine translations but also humans can collaborately check the results and work on creating the translations through a web interface manually. The models were trained specifically for legal and financial terminology.

### Challenges
- Ensuring confidentiality of the data

### Source
Interview conducted in May 2024, led by sebis researchers. The interviewee is the COO at the company.

AI Solution Provider: Neur.on AI

## AI Solution Provider: 913.ai

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### Who is using it?
The solution is targeted at legal service providers.

### What problem(s) are they solving?
913.ai ([https://www.913.ai/](https://www.913.ai/)) wants to provide an automation infrastructure that allows users (e.g., legal engineers) to connect various already existing tools in a law firm with each other. Use cases include but are not limited to document classification and information extraction. The solution aims to represent and assist in typical workflows in the daily legal work of lawyers.

### Which NLP technologies are they using?
913.ai uses open-source LLMs but also fine-tunes their own models for specific tasks like text extraction or classification. They offer a pipeline builder that allows users to select and link various modules together to depict their own unique scenarios as they need them. They are currently adding more modules and improving already existing ones to make them more adaptable by the users.

### Challenges
- GDPR Compliance
- Gaining new customers as customers in the legal domain tend to be more averse to cloud solutions

### Source
Interview conducted in April 2024, led by sebis researchers. The interviewee is a lawyer, co-founder and the CEO of the company.

AI Solution Provider: 913.ai

## AI Usage at a German Law Firm

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### Who is using it?

A large law firm with over 300 lawyers is evaluating the use of legal AI at their firm. The target audience of the tools are legal professionals.

### What problem(s) are they trying to solve?
The firm is interested in understanding the potential benefits of using AI in their practice, such as automating repetitive tasks, improving decision-making speed, and increasing overall efficiency. Therefore, the firm called into action an AI initiative consisting of multiple lawyers to explore and evaluate the use of legal AI.

The use cases under investigation include:
- Document analysis
- Document generation (e.g., contracts)
- Document summarization (e.g., summarizing court decisions)
- Due diligence
- Question Answering

For Question Answering, they are building a curated knowledge base containing all admissible company data that can then be queried through the chat interface. The semantic search also provides references to the sourced documents.

### Which NLP technologies are they using?
The law firm is evaluating the use of different LLMs including proprietary models like ChatGPT (OpenAI) and Luminous (Aleph Alpha), as well as open-source models like Llama.
For their daily work, all employees have access to ChatGPT deployed on their own Azure instance to ensure data is not used for training. To manage and reduce the costs associated with LLM usage, token length is limited.
Some users are also testing Copilot for their daily work needs.

### Stage
Production - different users are utilizing AI technology regularly in their workflows

### Challenges
- Costs of AI usage are not insignificant and need to be managed
- Employees need to be properly trained to ensure compliant usage of the AI solutions and maximize their usefulness
- The speed at which AI technology advances makes it difficult to keep up, e.g., context window increases (tokens)
- Societal implications, e.g., risk of fraud and scams

### Source
Interview conducted in March 2024, led by sebis researchers. The two interviewees are both lawyers at the law firm.

## LLM Usage at a Global Law Firm

-------------

### Who is using it?
The Mergers and Acquistions department of a large international law firm with roughly 70 lawyers in Germany (900 globally) is exploring the usage of AI in their daily work. The law firm has a global technology committee consisting of multiple employees including the CIO that evaluates new technologies and makes executive decisions about adopting them into the firm.

### What problem(s) are they solving?
The employees are using AI-based tools for document analysis, information extraction, and translation. They believe AI tools will eventually replace a lot of the work that is nowadays being done by junior associates.

### Which NLP technologies are they using?
The department is using Kira ([https://kirasystems.com/](https://kirasystems.com/)) for document analysis and information extraction from legal documents like contracts. The firm has access to its own instance running the software to ensure the confidentiality of the data.
For translations the lawyers use DeepL ([https://www.deepl.com/](https://www.deepl.com/)), an AI-based translation tool specifically trained for translation tasks.
Furthermore, the firm also tested ChatGPT but deemed it not good enough to be used as-is in their daily professional work and is now evaluating Harvey ([https://www.harvey.ai/](https://www.harvey.ai/)) for document analysis.

### Stage
Production - AI-based tools are used regularly in their legal work

### Challenges
- Ensuring GDPR compliance and confidentiality
- Trust in AI systems needs to be established
- Support for multiple languages is important for an international firm
- Not enough use cases (at the time of the interview) to justify large investments in AI
- Rethinking of business model (hourly billing vs. flat fee) might be necessary if AI increases efficiency

### Source
Interview conducted in February 2024, led by sebis researchers. The interviewee is a lawyer working in Mergers and Acquistions at a large law firm.

LLM Usage at a Global Law Firm

## AI Usage at a German Publishing Group

-------------

### Who is using it?
A large publishing group with its own legal department consisting of 6 lawyers is evaluating the usage of AI tools for their daily legal work.

### What problem(s) are they trying to solve?
The employees have access to a company-wide chatbot that was developed in-house and that they can use for their daily work. The use cases include:
- Document analysis
- Document summarization
- Text generation (e.g., email, rephrasing)
- Translation
- Question Answering
- Research

### Which NLP technologies are they using?
The company chatbot is based on models from OpenAI but does not have access to the internet or company data directly. However, users can upload documents through the chatbot interface to use them in ensuing prompts.
The company is also testing Semantha ([https://www.semantha.de/](https://www.semantha.de/)) for document analysis and semantic search.

### How do they test the quality of the tools?
The legal professionals do not have a predefined set of evaluation metrics but come together regularly to discuss the usage of AI and other relevant topics revolving around AI. The general sentiment is that there is a lot of potential in AI solutions but we are still just at the beginning and the tools currently do not work satisfactorily enough yet.

### Stage
Production - employees use company-wide chatbot for their daily work

### Challenges
- Hallucinations - LLMs do not always produce accurate information
- Finding the right AI solution(s) for each use case
- Ensuring that employees understand how to use the chatbot effectively through proper training
- Ensuring that company data is not used for AI training

### Source
Interview conducted in March 2024, led by Prof. Dr. Florian Matthes and Nektarios Machner. The interviewee is working at the firm's legal department as a lawyer with a focus on IT.

AI Usage at a German Publishing Group

## AI Usage at a German Law Firm
-------------

### Who is using it?
A German law firm with roughly 120 lawyers is exploring the use of AI in their daily legal work. The firm put together an AI Task Force consisting of multiple lawyers from the firm to evaluate AI-based tools regarding their applicability in their daily workflow.

### What problem(s) are they trying to solve?
The main use cases are document analysis and document generation, e.g., for documents submitted by a party in a legal proceeding or to create pitch decks. Further use cases include translation and general research. To assist with cases concerning intellectual property, an AI-based tool is used to do similarity analysis, e.g., comparing two logos or design.

Overview of use cases:
- Document analysis
- Document generation
- Translations
- Research
- Similarity Analysis

### Which NLP technologies are they using?
The law firm is currently evaluating ChatGPT to find out what for and how it can be used in their legal work. Additionally, Microsoft's Copilot is being explored as a personal assistant, e.g., to search for specific files on the file system. DeepL Pro ([https://www.deepl.com/](https://www.deepl.com/)), an AI-based translation tool specifically trained for translation tasks, is used for translations.

### How is data privacy cared for?
No privacy-sensitive data is used for testing to avoid any privacy-related issues.

### Stage
Production - lawyers use AI-based tools for their daily legal work

### Challenges
- Hallucinations - LLM output cannot be used as-is but needs to be double-checked and verified
- A lot of interesting AI solutions are put on the market but there is no proper overview to keep track of developments
- GDPR compliance, privacy and confidentiality need to be ensured
- AI solutions need to be integrated properly into the existing IT landscape and processes, which is not trivial for law firms without or with only a small IT department

### Source
Interview conducted in February 2024, led by sebis researchers. The interviewees are working as lawyers at the law firm.

# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
M&A lawyers and other legal professionals at a large German law firm are using legal AI tools, particularly within the context of document-heavy legal work such as due diligence and contract review. Legal tech professionals and lawyers with technical backgrounds support development and adoption.

### What problem(s) are they trying to solve? ###
They aim to reduce the time and effort required for document classification, summarization, and legal analysis during tasks like due diligence and litigation. AI is used to extract key contractual clauses, generate executive summaries, and assist with internal legal process improvements. There's also a focus on improving access to templates and internal resources via AI-assisted interfaces.

### Which NLP technologies are they using? ###
They are using large language models (LLMs), particularly GPT-4 and GPT-4o, in both internal (on-premise or country-hosted) and third-party tools. Cloud-based models are used cautiously and only when permitted. Tools include proprietary LLM-based chat interfaces, as well as domain-specific models like those integrated with legal publishers' databases. They also experiment with prompt engineering and use explanation-focused AI tools for model transparency.

### Stage ###
Production

### Challenges ###
- Hallucination risks in LLM outputs requiring careful review
- Low adoption rates due to lack of user understanding and training
- High effort for structuring internal data for AI usage
- Difficulties in aligning legal and technical teams
- Market pressure to reduce billable hours via AI without compromising quality
- Limitations in accessing comprehensive case law databases
- Lack of transparency in AI decision-making compared to human associates
- Change management issues and resistance from staff

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is a legal professional working at the organization.

Legal AI Usage at German Law Firm

# Legal AI Usage at German Technology Company

-------------

### Who is using it? ###
The legal department of a German technology company, consisting of eight in-house lawyers, is exploring the use of legal AI tools. Their work includes corporate law, M&A, and international contracts.

### What problem(s) are they trying to solve? ###
They aim to improve efficiency in handling legal documents and contracts. Specific use cases include translating legal texts, rephrasing clauses, summarizing lengthy documents (e.g., EU regulations), and creating initial drafts of legal clauses. A key goal is to automate the review and negotiation of standardized non-disclosure agreements (NDAs) to reduce lawyer involvement in routine processes.

### Which NLP technologies are they using? ###
They currently use on-premise chatbots based on open-source models such as LLaMa and Mixtral. Cloud-based models like ChatGPT are not permitted due to company policy. The department also experiments with proprietary chatbots embedded in legal databases (e.g., beck-online). Tools like DeepL are used for translation, and internally developed contract management systems support their workflow.

### Stage ###
Research

### Challenges ###
- Cloud-based AI models are blocked due to strict internal IT policies.
- Existing commercial tools were too costly relative to their benefits.
- Most contracts are unique and complex, limiting automation potential.
- High effort required to create playbooks or training data for AI tools.
- On-premise models lack legal domain training and access to legal databases.
- Technical limitations with document length and functionality.
- Need for better prompt engineering knowledge; planning internal training.
- Current tools cannot cite legal sources reliably.
- Manual verification of AI outputs is still necessary due to accuracy concerns.

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is a legal department manager and a legal counsel, working at the organization.


Legal AI Usage at German Technology Company

# Legal AI Education for German Legal Professionals

-------------

### Who is using it? ###
The users are primarily legal professionals in Germany, including lawyers, legal departments, and law students. Small law firms and individual practitioners show the most active interest.

### What problem(s) are they trying to solve? ###
Users aim to understand and adopt AI tools to improve efficiency in daily legal work. Specific problems include contract analysis, drafting and summarizing legal emails, document review, legal research, and time tracking for billing. The goal is to develop AI literacy and practical skills for integrating AI into legal workflows.

### Which NLP technologies are they using? ###
They use general-purpose models like ChatGPT and Perplexity, often in secure or self-hosted environments due to privacy concerns. Tools that mimic ChatGPT functionality but claim compliance with professional and data protection standards are also in use. Retrieval-Augmented Generation (RAG) systems are noted for document analysis tasks.

### Stage ###
Research

### Challenges ###
- Legal sector's general resistance to change and risk aversion  
- High skepticism regarding data protection and regulatory compliance  
- Lack of structured AI knowledge and technical expertise among lawyers  
- Difficulty in motivating adoption and routine use of AI tools  
- Structural inefficiencies and traditional workflows, especially in courts  
- Cost barriers for smaller firms  
- Media discontinuity and inconsistent software standards across jurisdictions

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is a legal tech entrepreneur and former product manager, working at the organization.

Legal AI Education for German Legal Professionals

# Legal AI Usage at German Consulting Firm

-------------

### Who is using it? ###
Legal AI tools are used by consultants and internal teams within the firm to support both client projects and internal legal workflows. The tools are also introduced and tested in client organizations through advisory projects.

### What problem(s) are they trying to solve? ###
The primary goals are to support text-based legal tasks such as document drafting, analysis, summarization, and information extraction. The firm also helps clients identify relevant use cases and integrate AI tools into their legal processes through workshops and proofs of concept. Legal AI is not seen as replacing legal professionals but as a tool to assist them.

### Which NLP technologies are they using? ###
The organization uses general-purpose generative AI models, particularly ones based on ChatGPT, with additional internal layers for customization. Tools mentioned include internally hosted generative AI chat systems and specialized legal AI assistants. Some tools rely on fine-tuning or prompt engineering to better address legal contexts. Most tools are cloud-based, though internal deployments are preferred for data-sensitive tasks.

### Stage ###
Production

### Challenges ###
- High user expectations vs. actual capabilities
- Identifying relevant use cases for each user group
- Integration into existing workflows
- Misunderstandings about what AI can and cannot do
- Issues related to hallucinations and knowledge scope
- Requirement for user education and change management
- Configuration complexity for specialized tools
- Slow adoption in conservative markets like Germany

### Source ###
Interview conducted in January 2025, led by sebis researchers. The interviewee is a legal technology consultant, working at the organization. 

Legal AI Usage at German Consulting Firm

# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
Lawyers, assistants, and secretarial staff across multiple practice groups at a German law firm are using legal AI tools. The digital strategy is coordinated centrally, with adoption managed and tracked across departments.

### What problem(s) are they trying to solve? ###
The firm aims to improve efficiency in legal workflows, particularly:
- Preparing case summaries for litigation
- Drafting documents and legal texts
- Proofreading and summarizing materials
- Internal knowledge management and document indexing

A core focus is on reducing time spent on manual processes and supporting legal work with AI-assisted summaries and drafting components.

### Which NLP technologies are they using? ###
The firm uses a multi-tiered approach:
- General-purpose chatbots (e.g., ChatGPT, Copilot) for experimentation and basic tasks like proofreading and drafting.
- More secure, legal-specific AI tools based on LLMs (some derived from ChatGPT) for sensitive tasks.
- Cloud-based solutions with a fallback to EU-hosted tools for data-sensitive clients.
- Ongoing pilot testing with additional models, including those for knowledge management.

They do not use enterprise licenses for public LLMs like ChatGPT but instead enforce strict usage guidelines and access restrictions.

### Stage ###
Production

### Challenges ###
- Hallucinations and inaccuracies in LLM outputs
- Poor performance of certain tools (e.g., Copilot in EU version)
- Low adoption or hesitation among conservative users
- Need for strict compliance with data protection laws (mandating internal usage guidelines and access gates)
- Difficulty achieving full-text draft quality with LLMs; users rely on building block-style drafting
- Scalability of custom tool development is limited due to firm size
- Managing and motivating sustained tool adoption across departments

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is a digital transformation lead, working at the organization.

# Legal AI Usage in Swiss Document Anonymization Software

-------------

### Who is using it? ###
The users include court departments, legal authorities, and administrative bodies ranging from small teams of five people to larger organizations with up to 200 employees.

### What problem(s) are they trying to solve? ###
The primary use case is the anonymization and pseudonymization of legal documents, such as court decisions. The goal is to automate suggestions for redactions to reduce manual effort, especially in large documents. Clients also seek to handle special formatting challenges in PDFs and accommodate jurisdiction-specific identifier structures, like those found in Austrian property designations.

### Which NLP technologies are they using? ###
They use on-premise NLP models for named entity recognition. The system is not based on generative models and does not rely on cloud services. The technology stack includes a base model trained on pseudonymized court decision data and additional logic using pattern matching (e.g., regex). They also make use of open-source components like spaCy.

### Stage ###
Production

### Challenges ###
- Complex formatting and non-machine-readable content in PDFs
- Balancing precision and over-redaction based on diverse client needs
- Limitations of OCR quality in source documents
- Client IT constraints and long deployment timelines
- High expectations due to public familiarity with tools like ChatGPT
- Legal-specific data handling requirements and slow cloud adoption in public institutions
- Resource limitations for small clients and aversion to subscription models

### Source ###
Interview conducted in June 2025, led by sebis researchers. The interviewee is a managing director, working at the organization.

Legal AI Usage in Swiss Document Anonymization Software

# Legal AI Usage by a Self-Employed Legal Professional in Germany

-------------

### Who is using it? ###
A self-employed legal professional with a background as both a lawyer and Legal Engineer, currently running her own law firm and advising other firms on digitalization.

### What problem(s) are they trying to solve? ###
AI is being used to streamline marketing and content creation (e.g., blog posts and videos), assist with legal research and brainstorming, and draft client communications. Additionally, the interviewee advises other firms, such as tax or compliance-focused organizations, on how to implement legal tech solutions to optimize workflows and automate repetitive tasks.

### Which NLP technologies are they using? ###
The interviewee uses ChatGPT (GPT-based) for brainstorming, writing support, and generating explanatory content. Legal research tools include AI-powered databases that summarize court decisions. There is experimentation with cloud-based tools, some of which also allow user-uploaded documents. Technologies mentioned are generally cloud-based.

### Stage ###
Research

### Challenges ###
- GPT models produce unreliable legal arguments and incorrect formatting in legal documents.
- High costs of legal research platforms limit accessibility for solo practitioners.
- Legal documents often cannot be shared with AI tools due to data protection concerns.
- Difficulty anonymizing court decisions for upload or use in tools.
- General lack of digital and AI readiness in parts of the legal profession.
- Resistance to change within firms due to psychological and organizational inertia.
- Many legal AI tools lack context awareness or require human oversight.

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is a self-employed legal professional with consulting experience in legal tech.

Legal AI Usage by a Self-Employed Legal Professional in Germany

# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
Lawyers at a large international law firm, especially those in areas like M&A, Private Equity, Real Estate, and Finance, are using legal AI tools. Usage varies by practice area and seniority.

### What problem(s) are they trying to solve? ###
They aim to improve efficiency in tasks such as document review, translation, and drafting standard contracts. AI is used for due diligence, generating draft memos, and benchmarking market standards. While high-end, bespoke legal work remains largely manual, AI supports standardization and consistency in more repetitive or commodity-oriented tasks.

### Which NLP technologies are they using? ###
They use general-purpose models such as ChatGPT and GPT-4, as well as a mix of external and proprietary tools. Some tools are integrated into cloud-based systems; others are custom-built or customized in-house. AI is employed for language translation, summarization, and drafting assistance, though bespoke legal advice is not generated by AI.

### Stage ###
Production

### Challenges ###
- Reluctance to share proprietary legal knowledge, even anonymized
- AI's limitations in handling complex, bespoke contracts
- Difficulty aligning AI-generated output with client-specific expectations
- High cost and maintenance of in-house tool development
- Concerns about consistency, accuracy, and overreliance on averages or correlations
- Resistance from experienced lawyers in high-end legal areas
- Difficulty in monitoring and evaluating numerous third-party legal tech vendors
- Legal industry’s emphasis on short-term profitability hampers long-term AI investments

### Source ###
Interview conducted in January 2025, led by sebis researchers. The interviewee is a senior lawyer, working at the organization.

# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
Lawyers at a large international law firm in Germany, particularly those in practice groups and legal tech roles, are using generative AI tools. A dedicated Legal Tech Fellow supports the integration and training of these tools across the firm.

### What problem(s) are they trying to solve? ###
They use legal AI tools for drafting documents (e.g., emails, memos, court pleadings), translating texts, explaining technical terminology, conducting preliminary legal research, and summarizing lengthy legal documents. The goal is to improve efficiency, reduce manual workload, and make legal knowledge more accessible across departments.

### Which NLP technologies are they using? ###
They primarily use Microsoft Copilot integrated with Office 365. Other tools such as ChatGPT and Perplexity have been tested, though Perplexity is used only privately due to data restrictions. They also co-develop specialized tools in collaboration with tech providers, such as a model for identifying legal risks in internal documents. Most tools are cloud-based, with strict attention to data protection and legal compliance.

### Stage ###
Production

### Challenges ###
- Difficulty integrating AI tools with existing systems
- Concerns over data security, especially with client data
- Limited accuracy in legal content generation (e.g., hallucinated citations)
- Prompting complexity and low intuitiveness for new users
- High effort required for data anonymization in model training
- Legal and regulatory constraints, especially in internal investigations
- Limited document upload sizes and processing constraints

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is a Legal Tech Fellow and practicing lawyer, working at the organization.


# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
Lawyers across different departments, especially in tax compliance, employment law, and real estate transaction teams. Some legal assistants and administrative staff also use AI-assisted tools for translation and reporting tasks.

### What problem(s) are they trying to solve? ###
The primary goals are to increase efficiency and reduce manual labor in document analysis, contract drafting, legal research, and translations. AI tools are used to assist with extracting data from large volumes of legal and financial documents, generating and managing standard contract clauses, improving translation quality, and drafting standard communications.

### Which NLP technologies are they using? ###
They are testing or using tools based on large language models like ChatGPT, especially through integrations with publishers (e.g., Otto Schmidt, Beck). Some tools operate via browser-based interfaces and cloud services. Cloud-based AI services (e.g., from OpenAI) are increasingly accepted as they adopt European data protection standards. Local hosting solutions were previously considered to ensure compliance with legal confidentiality requirements.

### Stage ###
Research

### Challenges ###
- Lack of centralized tool rollout across the firm
- Many tools still in trial or evaluation phase
- Legal and data protection concerns, especially around confidential client data
- System accreditation bottlenecks for cloud-based tools
- Tools often fail in complex or cross-border legal scenarios
- Limited benefit over manual research due to insufficient accuracy
- Internal resistance and delayed strategic alignment on AI adoption
- Difficulty integrating tools that require access to internal data repositories

### Source ###
Interview conducted in January 2025, led by sebis researchers. The interviewee is a senior legal professional, working at the organization.

# Legal AI Usage at a German E-Commerce Company

-------------

### Who is using it? ###
Legal operations and strategy teams within the corporate governance function are exploring and managing AI integration. Project managers and product leads, including non-technical staff, are involved in implementation.

### What problem(s) are they trying to solve? ###
- Improve efficiency in legal processes such as contract management and compliance.
- Enable self-service for frequently used documents like NDAs.
- Enhance searchability and usability of internal knowledge bases.
- Identify high-impact AI use cases for internal legal workflows.
- Reduce manual effort and dependency on disparate systems.

### Which NLP technologies are they using? ###
- Google Gemini, particularly the Notebook-LM feature, for general productivity, text editing, and internal chatbot creation.
- Conversational search features in a contract management tool are under consideration but not yet implemented.
- AI-powered search and chatbot functions in ServiceNow were explored, including a compliance chatbot based on company policy documents.
- All mentioned tools are cloud-based.

### Stage ###
Research

### Challenges ###
- Frequent delays and quality issues with external vendors during tool implementation.
- AI features in tools like Sirion and ServiceNow not yet activated due to ongoing basic setup and maintenance.
- Difficulty in identifying real efficiency gains without comprehensive quantification.
- Need for wider AI literacy and engagement among legal staff to identify relevant use cases.
- Ensuring AI tools fit into the existing tech landscape without adding unnecessary complexity.

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is a project lead working at the organization.

Legal AI Usage at a German E-Commerce Company

# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
Legal AI tools are used by lawyers across multiple departments at the firm, including M&A, corporate law, tax, litigation, and investigations. A dedicated legal innovation team coordinates the introduction and support of these tools.

### What problem(s) are they trying to solve? ###
The primary goal is to process large volumes of legal documents more efficiently, especially in data-heavy fields such as M&A due diligence and litigation. Tasks include summarizing and analyzing documents, drafting emails or memos, automating repetitive tasks, and structuring legal facts. Legal AI is also being explored for translation services and everyday legal workflows like deadline tracking and email correspondence.

### Which NLP technologies are they using? ###
The firm uses general-purpose GenAI models such as ChatGPT and Harvey. Harvey was selected for its superior performance in legal contexts. Other tools tested include CoCounsel. These are cloud-based systems. Traditional contract analysis tools are also in use, but GenAI tools are increasingly preferred for their broader capabilities.

### Stage ###
Production

### Challenges ###
- Ensuring sustained and consistent tool usage across the firm
- Overcoming forgetfulness or lack of integration into daily habits
- Addressing skepticism or limited trust in AI-generated outputs
- Evaluating the real efficiency gains and quality improvements quantitatively
- Supporting users with training and prompting best practices
- Managing organizational change and user onboarding (Change Management)

### Source ###
Interview conducted in January 2025, led by sebis researchers. The interviewee is a legal innovation associate, working at the organization.


# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
Lawyers at a mid-sized law firm, including partners, associates, and legal departments such as litigation, insurance law, tax law, and antitrust.

### What problem(s) are they trying to solve? ###
They aim to streamline legal workflows, reduce manual labor, and improve efficiency in legal tasks such as document summarization, case assessment, and contract analysis. Use cases include litigation file analysis, initial assessments in insurance cases, tax case documentation, antitrust contract checks, and audit-related contract review.

### Which NLP technologies are they using? ###
They use large language models (LLMs) such as GPT-3.5, GPT-4, Claude, LLaMa, and Gemini. These are cloud-based but integrated with an internal anonymization pipeline to comply with data protection requirements. Each use case is linked with specific LLMs and system prompts optimized for the task.

### Stage ###
Production

### Challenges ###
- Low initial adoption despite positive reception
- Need for extensive user training and support
- Fear of AI disrupting the billable hour business model
- High effort for anonymizing and uploading internal datasets
- IT resource bottlenecks and long implementation timelines
- Varied quality and reliability of model outputs
- Difficulty in convincing senior staff of the tool’s benefits
- Complex coordination and prompt engineering for use case development

### Source ###
Interview conducted in January 2025, led by sebis researchers. The interviewee is a lawyer and partner, working at the organization.

# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
The primary users are legal professionals within a major German law firm, including lawyers and legal transformation teams. Legal engineers and AI experts are also involved in tool evaluation and integration.

### What problem(s) are they trying to solve? ###
The tools are used to support legal work through automation and enhanced document handling. Key applications include translation, improving document quality, contract analysis, clause evaluation, data extraction, and legal research. There is also interest in using AI for drafting support and comparing legal texts or emails. Retrieval-Augmented Generation (RAG) systems are used for precise answers based on curated legal literature.

### Which NLP technologies are they using? ###
They use general-purpose language models like ChatGPT, as well as AI services via Microsoft Azure (e.g., Copilot). Both cloud-based and on-premise systems are employed, including proprietary setups for secure document querying. Beta testing has been conducted with models accessing curated legal data.

### Stage ###
Production

### Challenges ###
- High barriers for tool approval due to strict internal standards and product vetting
- Tools often selected by tech-savvy staff, but underutilized by end-users (lawyers)
- Low user adoption and resistance due to unfamiliarity or unrealistic expectations
- Misunderstanding of AI limitations, especially regarding hallucinations and legal accuracy
- Difficulty curating and generalizing internal legal knowledge for AI use
- Lack of transparency in AI tools (“black box” issue) impedes legal reliability
- Structural gaps in legal education to prepare for AI-assisted work

### Source ###
Interview conducted in January 2025, led by sebis researchers. The interviewee is a legal professional, working at the organization.

# Legal AI Usage at Bavarian Ministry of Justice

-------------

### Who is using it? ###
Legal AI tools are used by a dedicated unit within the Bavarian Ministry of Justice focused on Legal Tech and AI. The tools are intended for use by courts and public prosecutors across Bavaria. Internal staff, including legal professionals and administrative personnel, are involved in the development and testing of these tools.

### What problem(s) are they trying to solve? ###
The Ministry is addressing challenges related to the increasing volume of documents in civil and criminal law cases, such as long legal briefs and large-scale digital evidence (e.g., terabytes of data from searches). Key use cases include document search, metadata extraction, anonymization of legal texts, drafting support, and summarization of lengthy documents. Internally, AI is also used for improving text quality and creating visual materials for presentations.

### Which NLP technologies are they using? ###
They use and test generative AI models like large language models (e.g., ChatGPT-style tools) and have piloted a custom anonymization prototype based on a language model. Projects are developed in collaboration with academic partners and rely on open-source models trained on judicial data. Tools are generally self-hosted or developed in close cooperation with universities to comply with strict legal and data protection requirements.

### Stage ###
Research

### Challenges ###
- Handling massive volumes of digital data in legal proceedings
- Ensuring data quality and digital availability (e.g., OCR issues)
- Anonymizing personal data
- Strict regulatory constraints (e.g., storage location laws, AI Act obligations)
- Complexity of interdepartmental rollouts (e.g., electronic case files)
- Addressing societal trust and ensuring transparency in AI use
- Limitations of generative AI outputs, especially for visuals and bias
- Need for high AI literacy among staff to use tools critically

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is a legal professional working at the Bavarian Ministry of Justice.

Legal AI Usage at Bavarian Ministry of Justice

# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
The users are primarily corporate lawyers within the M&A department, including partners and associates. Additional involvement comes from litigation teams in the U.S. and global practice groups across the firm.

### What problem(s) are they trying to solve? ###
They aim to increase efficiency and speed in legal processes, such as document review, legal research, translation, and transaction management. Tools are used for information extraction, document drafting, summarization, and improving access to legal knowledge. They also support due diligence and e-discovery processes.

### Which NLP technologies are they using? ###
The firm uses extractive and generative AI tools, including general-purpose models like ChatGPT, especially within integrated platforms such as Juris-KI. They have tested tools like Kira and are preparing to roll out Westlaw Co-Counsel in Germany. DeepL is used regularly for legal document translation. Tools are primarily cloud-based, with strict security evaluations before deployment.

### Stage ###
Production

### Challenges ###
- High internal IT security and data protection requirements
- Lack of standardization in client AI usage policies
- Limited implementation efforts hindered tool adoption (e.g., Kira)
- Frustration with insufficient out-of-the-box performance of some tools
- Transparency concerns about data handling in AI tools
- Manual effort needed to verify AI-generated outputs
- Restrictions on AI features in communication tools due to legal risks

### Source ###
Interview conducted in January 2025, led by sebis researchers. The interviewee is a partner and an associate in the legal department, working at the organization.

# Legal AI Usage at German MedTech Company

-------------

### Who is using it? ###
The legal department, particularly in the IT legal function, is experimenting with AI tools. A small internal pilot team, including legal and IT staff, has tested tools such as ChatGPT and Microsoft Copilot.

### What problem(s) are they trying to solve? ###
The team is exploring AI tools for various legal support tasks, including:
- Drafting emails, PowerPoint slides, and speeches.
- Generating and reviewing legal documents (e.g., NDAs, development contracts).
- Running legal research and answering compliance-related questions.
- Reviewing long legal texts like EU regulations.
- Building internal legal chatbots (e.g., compliance Q&A bots).

### Which NLP technologies are they using? ###
They are using GPT-based models, specifically ChatGPT (commercial version), Microsoft Azure OpenAI (GPT-4), and Microsoft Copilot. The models are hosted in the cloud. They have not yet tested domain-specific tools like Harvey AI due to cost and availability concerns.

### Stage ###
Research

### Challenges ###
- High variability in output quality, especially for legal drafting and review.
- Tools fail to detect unusual or problematic contract clauses in longer documents.
- Inconsistent performance between tools (e.g., Azure OpenAI vs. Copilot).
- Legal hallucinations and unreliable responses in complex legal queries.
- Lack of efficiency gains for standard legal tasks.
- Data confidentiality and licensing concerns.
- Agents show promise but are still in early-stage testing.
- Legal risk tolerance is high as partial accuracy is insufficient in critical use cases.

### Source ###
Interview conducted in January 2025, led by sebis researchers. The interviewee is a legal counsel in IT law, working at the organization.

Legal AI Usage at German MedTech Company

# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
The primary users are legal professionals, including lawyers and legal departments within a large law firm affiliated with a major consulting organization. The interviewee oversees all AI-related initiatives at the firm.

### What problem(s) are they trying to solve? ###
They aim to streamline both legal and non-legal workflows. Use cases include document analysis (e.g., contract review), searching and extracting clauses, improving internal efficiency, accelerating legal assessments (e.g., compliance), and handling large-scale litigation by navigating massive document collections. The tools are also used for tasks like generating offers, board reports, and verifying NDAs or codes of conduct.

### Which NLP technologies are they using? ###
They use general-purpose language models such as ChatGPT and GPT-based systems. Tools are a mix of proprietary and customized applications, built on cloud platforms like Microsoft. Their solutions include:
- A customizable chatbot interface with agent chaining
- A self-developed contract assessment tool using retrieval methods and generative AI
- An AI agent framework for orchestrated document analysis in investigations and litigation

### Stage ###
Production

### Challenges ###
- Low initial user acceptance and difficulty encouraging engagement
- Need for highly tailored internal training to match specific legal fields
- Fragmented internal knowledge bases across legal teams
- Organizational resistance to new workflows
- Technical vendors lacking legal domain understanding
- Difficulty in knowledge curation for effective RAG systems

### Source ###
Interview conducted in January 2025, led by sebis researchers. The interviewee is a legal professional with a technical background, working at the organization.


# Legal AI Usage at Global Sportswear Company

-------------

### Who is using it? ###
In-house legal professionals at the corporate headquarters, particularly those supporting global IT, e-commerce, loyalty programs, data analytics, and AI compliance.

### What problem(s) are they trying to solve? ###
They aim to streamline legal workflows such as contract generation and review, translation, drafting non-standard contract templates, and email composition. Additionally, AI is used for summarizing meetings and providing structured outputs from long discussions. A future tool is expected to extract contract metadata to populate a database.

### Which NLP technologies are they using? ###
They are testing general-purpose cloud-based models such as Microsoft Co-Pilot (integrated with Microsoft 365) and a legal chatbot from beck-online. They are also exploring a contract software tool with AI-based data extraction capabilities.

### Stage ###
Research

### Challenges ###
- Limited availability of permanent licenses for tested tools
- Legal chatbot only supports certain areas of law and is language-specific
- AI outputs require verification to avoid hallucinations
- Users must be trained to critically evaluate AI responses
- Prompt quality significantly affects output usefulness
- High expectations can lead to frustration due to technical limitations
- Energy/resource usage concerns tied to widespread AI deployment

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is an in-house legal professional, working at the organization.

Legal AI Usage at Global Sportswear Company

# Legal AI Usage at Mid-Sized German Law Firm

-------------

### Who is using it? ###
Lawyers and partners at a mid-sized law firm, especially those involved in corporate law and M&A. A small internal AI team, including a few partners and employees, is responsible for tool evaluation and testing.

### What problem(s) are they trying to solve? ###
The firm uses legal AI to support contract drafting, markup review, document screening for due diligence, summarization, legal research, and translation. AI also serves as a sparring partner in negotiations and helps structure legal arguments. Another goal is to reduce administrative work, such as client onboarding.

### Which NLP technologies are they using? ###
The firm uses ChatGPT (free and licensed versions) and Claude via third-party tools for general legal language tasks. Tools like Libra, LangDoc, and Spellbook are in testing. These rely on cloud-based large language models.

### Stage ###
Research

### Challenges ###
- Lack of technical infrastructure to ensure data privacy (e.g., anonymization, document redaction)
- Risk of hallucinations and factual inaccuracies in AI outputs, especially for legal norms and case law
- Limited internal expertise and resources for tool development or integration
- Low adoption due to unfamiliarity or resistance among employees
- Difficulty verifying AI-generated results without sufficient legal experience
- Restricted access to legal databases limits AI effectiveness
- Time constraints hinder extensive tool evaluation
- Fragmented tool landscape with varying stability and usefulness

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is a partner and legal professional, working at the organization.


Legal AI Usage at Mid-Sized German Law Firm

# Legal AI Usage at a German Legal Insurance Subsidiary

-------------

### Who is using it? ###
Employees of a legal insurance subsidiary are involved in evaluating and implementing AI use cases. Legal services are primarily delivered through cooperation with external law firms and partners. Internal users are mostly part of service and claims departments.

### What problem(s) are they trying to solve? ###
The organization aims to automate legal claim evaluations, contract coverage checks, and reimbursement decisions (e.g., court or attorney costs). Use cases include extracting key information from legal documents, supporting legal research, and enabling more efficient internal processes. AI is also used indirectly through partner law firms for tasks like legal document drafting and analysis.

### Which NLP technologies are they using? ###
They use generative language models and prediction models, often as cloud-based licensed solutions. The models are integrated into broader systems for document processing and legal evaluation. There is also experimentation with on-premise and partially customized tools. External law firms employ tools based on large language models such as ChatGPT or GPT-4.

### Stage ###
Research

### Challenges ###
- Hallucinations and lack of precision in legal contexts
- Legal restrictions on providing legal advice internally
- Data protection concerns and GDPR-related limitations
- Difficulty in verifying outputs, especially for junior staff
- Complex integration with legacy systems
- High upfront effort for anonymization and data preparation
- Lack of suitable tools for civil-law (non-English) jurisdictions
- Risk of decreasing legal training quality among new professionals

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is a managing director working at the organization.

Legal AI Usage at a German Legal Insurance Subsidiary

# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
Legal Tech practitioners within the law firm, including attorneys who integrate legal technology into client matters. A dedicated Legal Tech team coordinates tool procurement, implementation, and internal/external innovation processes.

### What problem(s) are they trying to solve? ###
The firm uses legal AI to improve efficiency in areas such as document review (e.g., eDiscovery and M&A due diligence), contract analysis, drafting, summarization of court decisions, and translation. AI also supports internal collaboration, document automation, and the creation of chronologies for fact-finding. The overarching goal is to enhance productivity and maintain competitiveness while meeting client expectations for efficient service.

### Which NLP technologies are they using? ###
They use both commercial and self-developed tools. General-purpose LLMs (e.g., GPT-based systems like Harvey) are used for legal content generation and review. Their custom tool is built on Azure services and performs document processing, search, and RAG-based QA. The models are hosted in the EU and designed without continuous learning, ensuring confidentiality. Other machine learning-based tools are also employed for M&A and document classification tasks.

### Stage ###
Production

### Challenges ###
- Compliance with strict German professional secrecy laws, requiring extensive vendor obligations.
- Internal coordination and documentation for custom tools are resource-intensive.
- LLMs struggle with deeper context understanding, document structure, and logical coherence.
- Identifying and communicating effective use cases across the organization remains challenging.

### Source ###
Interview conducted in January 2025, led by sebis researchers. The interviewee is a Legal Tech Manager and practicing attorney, working at the organization.

# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
A legal tech and digital transformation unit within a large law firm, consisting of jurists and IT-affine staff, is leading the AI implementation. The team includes one programmer and works closely with selected external service providers.

### What problem(s) are they trying to solve? ###
They aim to address repetitive legal workflows, quantitative document analysis, and efficiency improvements. Use cases include automation of contract generation, processing of routine administrative tasks, and document comparison. The overarching goals are reducing workload, minimizing errors, and driving innovation.

### Which NLP technologies are they using? ###
They are testing and integrating external and local large language models, including LLaMa, via APIs. Machine learning is used for quantitative analysis, and Robotic Process Automation (RPA) is also being implemented. Hybrid approaches combining ML and RPA are under development. These tools are introduced in a technology-neutral and problem-driven manner.

### Stage ###
Research

### Challenges ###
- High resource demands: time, cost, and organizational change
- Need for internal training and cultural acceptance
- Complexity of integrating multiple technologies into existing infrastructure
- Resistance to adoption without clear communication of benefits
- Lack of prompting methodology in early tool rollouts
- Vendor overselling during AI hype phases
- Regulatory and compliance concerns depending on tool use

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is the head of digital transformation and legal tech, working at the organization.

# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
Lawyers across all levels (from associates to partners) and staff members, especially assistants, are using legal AI tools. A dedicated Legal Tech team, including in-house developers, supports implementation and customization.

### What problem(s) are they trying to solve? ###
The firm uses legal AI primarily to summarize legal documents, assist with drafting emails and first drafts of pleadings, and perform document analysis. They aim to reduce administrative burdens, improve efficiency, support knowledge management, and offer clients AI-enhanced legal services. Future plans include integrating internal systems for unified access and creating tools tailored to the firm's specific legal workflows.

### Which NLP technologies are they using? ###
They are using legal and general-purpose large language models, including Harvey, ChatGPT and Microsoft Copilot. Some tools are hosted in the cloud with data segregation ensured through contractual safeguards. Internal prototypes leverage LLMs, and software development is accelerated using AI-assisted coding tools like GitHub Copilot.

### Stage ###
Production

### Challenges ###
- Limited access to legal databases due to restrictive licensing from publishers.
- Initial skepticism and resistance from lawyers unfamiliar with AI capabilities.
- Lack of creativity among lawyers in identifying potential use cases.
- Significant variation in internal workflows makes standard tool adoption difficult.
- Technical limitations in Microsoft Copilot’s integration with existing infrastructure.
- Difficulty validating references to legal precedents using current tools.
- High effort required to ensure legal AI outputs meet quality standards.
- Resource overhead and risk of knowledge leakage when outsourcing tool development.

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is a Director of Legal Tech, working at the organization.


# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
The tool is used by legal professionals including lawyers, business support staff, marketing, HR, and secretarial teams at a large international law firm. Usage is voluntary and access is available firm-wide to those who request it.

### What problem(s) are they trying to solve? ###
The tool is primarily used for drafting and refining legal clauses, generating legal memos, and formulating native-level English text (e.g., emails or documents). It helps with clause modifications, inspirations for restructuring contracts, and abstract legal analysis. In specific cases, it is also used to explore contract risks and options in restructuring scenarios. Beyond legal staff, non-legal teams use it for text generation tasks.

### Which NLP technologies are they using? ###
They are using Harvey, a proprietary chatbot interface based on large language models, trained with legal data. It allows document upload and context-aware interactions. Comparisons were made with ChatGPT and Microsoft Copilot, which is also being introduced. Usage is cloud-based but with contractual attention to data privacy and confidentiality.

### Stage ###
Production

### Challenges ###
- Data privacy and confidentiality concerns, especially under legal professional duties across multiple jurisdictions.
- Limited reliability when analyzing or generating content based on uploaded documents.
- Cannot yet draft complete contracts from term sheets.
- Quality limitations with German legal language and jurisdiction-specific clauses.
- Users must verify all AI outputs, as results may be confidently incorrect.
- Legal staff must be involved in tool implementation to ensure usability.
- Limited integration compared to tools embedded directly in office software (e.g., Copilot in Word/Outlook).

### Source ###
Interview conducted in December 2024, led by sebis researchers. The interviewee is a legal professional, working at the organization.

# Legal AI Usage at German Law Firm

-------------

### Who is using it? ###
Lawyers in the corporate law department, particularly those involved in M&A and venture capital transactions, are exploring and using legal AI tools. A dedicated internal AI team (approximately 8–9 people) supports these efforts.

### What problem(s) are they trying to solve? ###
The main goals are to improve efficiency in legal workflows, particularly in translation, document review (e.g., due diligence), summarization, and internal research. AI is also used to assist with tasks such as preparing presentations and navigating legal databases more efficiently.

### Which NLP technologies are they using? ###
They use general-purpose NLP models like ChatGPT and GPT-based tools. Tools are both cloud-based and on-premise. ChatGPT is used privately for general legal brainstorming, while Co-Pilot is tested for tasks like presentation and spreadsheet support. Tools such as DeepL are used extensively for document translation. Other tools mentioned include those for legal document extraction and comparison.

### Stage ###
Research

### Challenges ###
- Data privacy and confidentiality concerns
- High tool acquisition and implementation costs
- User adoption varies by personality, experience, and willingness to change
- Difficulty in integrating tools into the daily workflow
- Need for internal testing capacity and time
- Quality control, especially in consistent legal terminology during translation
- Risk of hallucinated results from some models (e.g., fictitious case law citations)

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is a legal professional working at the organization.

# Legal AI Usage in a Corporate Legal Department

-------------

### Who is using it? ###
In-house legal counsel specializing in digital law, including IT procurement, data protection, and compliance with the EU AI Act. The tools are primarily used by legal professionals within the corporate legal department.

### What problem(s) are they trying to solve? ###
Legal AI tools are used to improve efficiency in legal research, document drafting, summarization of lengthy communication threads, and the preparation of presentations. Specific applications include drafting contracts, creating summaries from email histories, and assisting with internal compliance documentation related to data protection and the AI Act.

### Which NLP technologies are they using? ###
They use a mix of general-purpose and legal-specific LLM tools. These include:
- A chat function integrated into a legal database for research purposes.
- Microsoft Copilot for general-purpose AI tasks like text generation and summarization within the Office suite.
- A legal-specific LLM trained on European and German court decisions and some legal commentaries, used particularly for data protection and IT law.

All tools mentioned are cloud-based. ChatGPT is not officially approved for internal use, though it may be used privately or for non-confidential queries.

### Stage ###
Production

### Challenges ###
- Incomplete or imprecise responses from legal database chat tools, especially outside of their core legal domains.
- Difficulty accessing specific court decisions via the research chat interface.
- Need for high-quality prompt engineering to ensure accurate outputs.
- Ongoing requirement to manually verify AI-generated content.
- Restrictions on feeding personal or sensitive data into tools due to data protection concerns.
- Legal and organizational scrutiny around vendor selection and data residency (e.g., avoiding data transfer outside the EU).
- Limited internal authority of legal users over procurement and deployment decisions.

### Source ###
Interview conducted in February 2025, led by sebis researchers. The interviewee is a legal professional, working at the organization.

Legal AI Usage in a Corporate Legal Department

# Legal AI Usage at German Law Firm

-------------

### Who is using it?
Knowledge Management lawyers and antitrust/competition lawyers within a large German law firm are using the tools. The firm also operates a dedicated legal tech venture that develops client-facing solutions and handles large-scale investigations.

### What problem(s) are they trying to solve?
The primary goals are knowledge management, automating administrative workflows, and assisting with business development. Specific use cases include generating internal newsletters on case law updates (which reduced the user's manual drafting time from 12 hours to 1 hour), automatically structuring event/webinar invitations from emails, and drafting legal publications. They are also experimenting with custom AI agents to act as "junior associates" for first-draft legal analysis using publicly available EU antitrust guidelines.

### Which NLP technologies are they using?
The firm officially provides Microsoft Copilot and an internal proprietary GPT-wrapper. However, to bypass the limitations of general-purpose tools, the user builds Custom GPTs via ChatGPT (utilizing the Code Interpreter function). They also use Gemini and Claude for specific tasks. To ensure highly precise citations and prevent probabilistic paraphrasing, public legal texts are heavily pre-processed and structured into JSON formats (defining articles, paragraphs, and sentences) before being fed into the models. 

### Stage
Production

### Challenges
* Lack of precision and traceability in off-the-shelf AI models, which fail to meet the exactness required in continental European law.
* High manual effort required to properly structure input data (e.g., JSON formatting) to force the AI to cite sources correctly.
* Building trust among senior lawyers to delegate tasks to AI in the same way they would to junior associates.
* Tool fatigue and user overwhelm due to the rapid introduction and deprecation of different AI solutions.
* Strict data protection policies that prevent the use of sensitive client data in third-party, cloud-based models.

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is a Knowledge Management Lawyer specializing in antitrust law, working at the organization.

# Legal AI Usage at German Law Firm

-------------

### Who is using it?
Lawyers across various decentralized departments, specifically in Real Estate, Tax, and M&A, as well as a Digital Transformation Officer who coordinates IT and digital processes. Tool adoption is largely bottom-up, driven by individual or departmental needs rather than a top-down, firm-wide mandate.

### What problem(s) are they trying to solve?
The firm uses AI primarily to process, review, and structure large volumes of legal documents efficiently, particularly for M&A due diligence and complex asset management. Specific use cases include preliminary legal research, summarizing and structuring historical dispute documents, quickly analyzing foreign legal texts (e.g., US Trust deeds) for German tax implications, and automatically generating corporate structure charts from text or handwritten notes. AI is also used for broader organizational tasks.

### Which NLP technologies are they using?
They utilize a diverse, continually evolving stack of both legal-specific and general-purpose tools. For legal research, they use publisher-integrated AI assistants like *Otto Schmidt Answers*, *BeckChat*, and *Juris KI*. *Legora* is heavily utilized for document review and data preparation, while *Noxtura* is currently in a testing phase. *Jigsaw AI* is employed to generate PowerPoint organizational charts. Additionally, they use general off-the-shelf LLMs like *ChatGPT*—having found that domain-specific tax models do not consistently outperform them—and have fully rolled out *Microsoft Copilot* for general administrative and organizational workflows.

### Stage
Production

### Challenges
* High rates of hallucinations and factually incorrect outputs in some tools during testing.
* Domain-specific (e.g., tax-focused) AI models often fail to provide better results than general-purpose LLMs.
* Difficulty maintaining continuous benchmark testing; tools are evaluated sporadically, making it hard to track long-term performance improvements.
* High initial organizational investment required for IT integration, data privacy compliance, and vendor negotiations.
* AI struggles to automate workflows where the required output is highly standardized but the input documentation is extremely diverse and unstructured.
* Lack of a structured internal AI training and education program to keep lawyers informed about new capabilities and best practices.

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewees are a Digital Transformation Officer and a practicing Lawyer specializing in Real Estate and Tax, working at the organization.

# German Legal AI Software Provider

-------------

### Who is using it?
The tools are developed for and used by lawyers in both large law firms (Big Law) and in-house legal departments of major corporations. Users range from highly specialized technical legal engineers and innovation teams to individual practitioners across various fields, including employment law, insolvency, and criminal law.

### What problem(s) are they trying to solve?
The provider offers two main solutions to address different legal needs. The first is a productivity tool for individual lawyers to assist with document-heavy tasks like summarization, comparison, drafting, and legal research. The second is a low-code/no-code automation platform that allows firms to transform manual processes into digital applications without deep coding knowledge. Key use cases include automated intake, self-service tools for clients (e.g., "Dawn Raid" apps), NDA automation, and streamlining repetitive administrative "office work" like client communication and file management.

### Which NLP technologies are they using?
The provider utilizes Generative AI (GenAI) and Large Language Models (LLMs), primarily integrating established American models due to their superior performance. They employ a mix of technologies, including semantic search, Retrieval-Augmented Generation (RAG) grounded in specialized legal databases (e.g., Otto Schmidt), and custom-trained anonymization techniques to handle sensitive data. The platform also features "Poka-Yoke" (error-proofing) mechanisms, such as visual validation markers (green checkmarks) that verify AI-generated citations against actual case law and statutes.

### Stage
Production

### Challenges
* High heterogeneity in user technical literacy and prompting skills across the legal market
* Difficulty for users in calculating a clear Return on Investment (ROI) for AI tools
* Significant gap in AI adoption and digital readiness between law firms and corporate legal departments
* Cultural resistance from senior partners who may lack a strategic incentive to adopt new technology
* Risk of "Imposter Syndrome" among lawyers who underestimate their value-add beyond simple legal research
* Overcoming the "Trough of Disillusionment" as users realize the limitations of GenAI
* Ensuring data privacy while utilizing powerful but cloud-based American LLM infrastructures
* Technical challenges in integrating AI into complex, transaktional workflows like M&A

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is the CEO/founder of a legal technology company providing AI-based automation and productivity tools.

German Legal AI Software Provider

# Legal AI Usage at Large International Law Firm

-------------

### Who is using it?
Legal professionals in the IT law and digitalization practice group at a large international law firm. The users focus on IT law, data protection, and EU digital strategy compliance (e.g., AI Act). 

### What problem(s) are they trying to solve?
The tools are used to act as a "sparring partner" for legal brainstorming and to streamline daily workflows. Specific use cases include initial legal research, drafting and adjusting the tone of emails and pleadings, and restructuring standard contracts (e.g., works council agreements). A major solved problem is translation; AI is now used to provide "convenience translations" for international clients, which are faster and cheaper, completely replacing the firm's former internal translation department. The tools are also used for creating presentations and daily organizational tasks (e.g., email and to-do list management).

### Which NLP technologies are they using?
They use a combination of legal-specific databases and general-purpose large language models. Legal tools include Juris KI and publisher-specific bots (e.g., Beck's "Frag den Grüneberg"). For drafting and structural tasks, they rely heavily on Claude and Google Gemini. Because the firm does not have specific enterprise data agreements for these public LLMs, all inputs must be strictly anonymized manually. Microsoft Copilot is used within a protected environment, primarily for organizational tasks. Specialized legal AI tools like Harvey are not currently available due to pending global committee strategy decisions.

### Stage
Production

### Challenges
* Strict professional secrecy and data protection laws require manual, time-consuming anonymization of prompts for public LLMs.
* AI outputs still contain dogmatic errors, incorrect legal interpretations, and hallucinations, requiring careful manual review.
* A noticeable decline in the learning curve of junior lawyers, who tend to blindly trust AI outputs without developing foundational legal research skills or critical thinking.
* Unresolved liability and risk concerns regarding reliance on AI-generated work products, especially in high-stakes transactions.
* Slow rollout of advanced legal AI tools due to centralized, global committee approval processes.

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is an IT law professional and associate working at the organization.

Legal AI Usage at Large International Law Firm

# Legal AI Usage at Global Law Firm

-------------

### Who is using it?
Lawyers across the firm globally and in Germany, particularly within data-heavy practice groups such as Litigation, Dispute Resolution, and Corporate/M&A. Adoption varies, with approximately 20% being heavy users, 30% occasional users, and the rest either testing sporadically or refusing to use the tools.

### What problem(s) are they trying to solve?
The firm uses AI primarily to handle massive volumes of unstructured data. Key use cases include extracting facts to build case chronologies in litigation, document review, and due diligence in corporate transactions. AI is also used for translation, general text correction (e.g., finding formatting errors or incomplete sentences), and as a brainstorming partner. The firm actively avoids using AI for drafting standard contracts, as their extensively maintained, automated internal templates yield superior and more consistent results.

### Which NLP technologies are they using?
They use a wide mix of generative AI and traditional deep learning tools. Generative AI tools include a custom ChatGPT environment hosted in Microsoft Azure, Microsoft Copilot, and Harvey. Specialized tools include DeepL for translation, Wechsler for fact extraction and chronologies, Orbital Witness for real estate, and Vlex for case law research in the US. They also rely heavily on trained deep learning systems like Kira and Relativity for document analysis, which currently outperform non-trained GenAI models in specific extraction tasks. 

### Stage
Production

### Challenges
* High financial costs associated with specialized legal AI tools
* AI-generated drafts are inconsistent and cannot reliably distinguish between nuanced legal positions (e.g., buyer-friendly vs. seller-friendly clauses)
* GenAI outputs often sound convincing but lack deep domain expertise and frequently miss the core legal issue
* Risk of severe reputational damage if overworked lawyers blindly trust and submit unverified AI outputs
* Automating foundational document review tasks removes crucial training opportunities for junior lawyers
* Restrictive terms of use from legal publishers (e.g., Beck, Juris, EUIPO) prevent the firm from crawling databases to train internal tools
* An oversaturated market of AI vendors leads to tool fatigue and high switching costs for only marginal improvements

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is an IT and legal tech professional working at the organization.

Legal AI Usage at Global Law Firm

# Legal AI Usage at Full-Service German Law Firm

-------------

### Who is using it?
AI tools are used by over 60% of the firm's personnel, including lawyers, associates, business professionals, and legal trainees (Referendare). While available firm-wide, usage and pilot programs are heavily focused on four key standardizable practice groups: Corporate, M&A/Real Estate M&A, Conflict Resolution (Litigation), and Employment Law.

### What problem(s) are they trying to solve?
The firm focuses on three main pillars for AI application: text creation/drafting, data analysis (such as reviewing large volumes of documents for due diligence), and legal research. Translations are also a common use case. A broader organizational goal is to establish AI-native workflows, adapt training programs so junior staff learn to verify AI outputs rather than perform manual searches, and prepare for future collaborative AI environments with clients.

### Which NLP technologies are they using?
The firm employs a deliberate "multi-lane strategy" rather than relying on a single one-size-fits-all provider. They provide base licenses for major legal AI platforms (Harvey or Legora) and Microsoft Copilot to their staff. They supplement these with specialized tools (like Noxthur) for specific research or data analysis tasks, and integrate smaller, domain-specific tools capable of querying specialized German and EU legal databases (e.g., Beck, Otto Schmidt).

### Stage
Production

### Challenges
* Lack of creativity among some lawyers in identifying AI use cases, causing them to revert to manual workflows.
* A small, rigid group of employees who remain entirely resistant to adopting the technology.
* Frustration with smaller, regional AI vendors that attempt to build generalist tools instead of focusing on their highly specialized niche strengths.
* Significant gaps in university legal education, requiring the firm to build its own internal AI and commercial training modules for trainees.
* Difficulties for general models to reliably access and integrate specific German or EU-level case law data without specialized database access.

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewees are legal innovation and transformation professionals working at the organization.

Legal AI Usage at Full-Service German Law Firm

# Legal AI Usage at International Law Firm

-------------

### Who is using it?
M&A lawyers and other legal professionals across multiple jurisdictions (EMEA, Americas, Asia) at a large international law firm. The global evaluation and rollout of tools are coordinated centrally by a dedicated innovation team, overseen by a Chief Innovation Officer, with localized training groups supporting adoption.

### What problem(s) are they trying to solve?
The firm aims to process massive volumes of documents in virtual data rooms that are no longer feasible to review manually. AI is used for due diligence analysis, generating contract markups and issue lists, and drafting smaller contracts and legal memos. Additionally, AI serves as a quality assurance tool to review contracts for clarity and inconsistencies, and to help structure legal arguments. 

### Which NLP technologies are they using?
They primarily use a specialized legal AI platform called "Ligora" (partially powered by Claude) which is integrated directly into Microsoft Word. General-purpose models like ChatGPT (referred to internally via a system called Atlas) are also utilized, though with caution. Other tools tested or in use include Microsoft Copilot, Harvey, and specialized databases like Beck next*tour for German law research. The firm is highly interested in moving toward "Agentic AI" to integrate directly with internal document management systems like iManage and Outlook.

### Stage
Production

### Challenges
* High incidence of hallucinations when using non-specialized models (e.g., standard ChatGPT).
* Severe formatting issues with AI-generated text in Word, requiring heavy manual cleanup by secretarial staff.
* Difficulties in user adoption and change management; moving lawyers from traditional research to prompt engineering requires intensive, localized training.
* Client resistance and strict confidentiality concerns, with many clients refusing consent for their data to be processed by US-based cloud AI models.
* Current AI tools lack deep integration across the firm's software ecosystem (e.g., inability to autonomously sort 500+ daily emails or seamlessly query the iManage database).
* Specialized tools sometimes lack localized legal data (e.g., currently limited to EU law without deep German legal integration).
* High financial costs for licenses, server infrastructure, and support, which indirectly drive up billable rates.

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is an M&A lawyer and Lead Innovation Partner for EMEA at the organization.

Legal AI Usage at International Law Firm

# Legal AI Usage at German Law Firm

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### Who is using it?
Lawyers at a large German law firm, particularly those specializing in M&A and finance. An internal AI task force meets monthly to coordinate the firm's AI strategy, evaluate tools, and drive adoption. Tool procurement is decided by the partnership. While usage is becoming widespread, adoption levels vary, ranging from skeptical older partners to dedicated "power users."

### What problem(s) are they trying to solve?
The primary goals are time savings, structuring complex thoughts, and general convenience. In M&A and finance, AI is used to analyze large data rooms—such as extracting "Change of Control" clauses from hundreds of documents simultaneously. It is also used to draft initial contracts, check existing drafts for logic and systematic errors, summarize large fact patterns into bullet points, and generate executive summaries based on due diligence reports. Additionally, the tools are used to extract relevant passages from legal literature for writing legal commentaries.

### Which NLP technologies are they using?
The firm relies heavily on Harvey (specifically utilizing its "Vault" feature for analyzing large document batches) and Microsoft Copilot (used more like a chat assistant for quick queries or brainstorming). Both tools are secured via enterprise agreements to ensure client data is not used for model training. ChatGPT was used early on but was abandoned to avoid the manual effort of anonymizing data. The firm tested Beck NoxxTura but found the AI capabilities lacking, and they are preparing to pilot Google Gemini soon.

### Stage
Production

### Challenges
* High risk of hallucinations presented with extreme confidence (e.g., confidently stating incorrect form requirements for GmbH share transfer proxies).
* Models like Copilot hallucinating non-existent court decisions and falsely claiming they were found on databases like Juris.
* Complete inability of generative AI tools to output text in the firm's specific formatting or Word templates.
* AI writing style is often un-natural, overly flowery, or relies on unusual punctuation (e.g., excessive colons and dashes).
* A structural gap in the German market: good AI models lack access to legal databases, while good databases have inferior AI models.
* Generational resistance to adoption among older staff members who lack trust in AI.
* Output requires continuous, manual verification due to reliability concerns in complex legal matters.

### Source
Interview conducted in April 2026, led by sebis researchers (TU Munich). The interviewee is an M&A lawyer and active AI power user, working at the organization.

# Legal AI Usage at a Large German Law Firm

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### Who is using it?
Lawyers specializing in Intellectual Property (IP), Trademark, and Competition Law at a large law firm. The users include partners, associates, and legal assistants (WissMits), supported by an internal legal tech team and dedicated software developers.

### What problem(s) are they trying to solve?
The firm aims to enhance the quality and depth of legal work while increasing efficiency in research and drafting. Key use cases include factual research to explain technical products in trademark similarity cases, preparing for client meetings based on vague email descriptions, and analyzing large volumes of court decisions (e.g., EuG rulings). It is also used to summarize lengthy legal chapters into executive summaries, refine the linguistic precision of arguments, and automate administrative tasks such as meeting protocols, email searches, and converting Word documents into presentation slides.

### Which NLP technologies are they using?
The firm utilizes a diverse stack of general and specialized tools. General models include Gemini (specifically the Deep Research feature) and ChatGPT. Microsoft Copilot is deeply integrated into the Office 365 environment (Teams, Word, Outlook, PowerPoint). Specialized legal research is conducted via Beck-Nox Tour, and notebookLM is used for analyzing public datasets. Crucially, the firm uses "CRAG" (Client Refined AI Generator), an internally developed, secure AI tool built on OpenAI APIs hosted in a private cloud to ensure data protection and professional secrecy.

### Stage
Production

### Challenges
* Hallucination risks requiring rigorous "Human in the Loop" verification for all outputs
* Widespread fear and reluctance among the legal profession to start adopting AI tools
* The "Jagged Frontier" of AI performance, where models excel at complex tasks but fail at simple ones
* Difficulty in identifying the correct specialized tool for specific legal vs. factual use cases
* A cognitive bottleneck as the workload shifts from content production to high-volume review
* Risk of declining training quality for junior lawyers as entry-level drafting tasks are automated
* Economic pressure on the billable hour model as clients refuse to pay for automated drafting time
* Maintaining professional secrecy and data privacy when using non-internal tools
* Difficulty in managing the transition from legal "craftsman" to "manager of AI agents"

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is a lawyer and legal tech expert working at the organization.

Legal AI Usage at a Large German Law Firm

# Legal AI Usage at Large International Law Firm

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### Who is using it?
Experienced M&A lawyers, particularly those specializing in complex, cross-border private equity and insurance transactions, at a large international law firm with over 2,500 employees. Junior associates also use the tools for foundational groundwork and initial document drafting.

### What problem(s) are they trying to solve?
The primary goal is improving the quality and precision of legal products rather than solely focusing on efficiency or reducing billable time. Key use cases include refining the idiomatic flow of English emails, acting as a sparring partner for complex contract drafting, summarizing massive document sets (e.g., thousands of pages for regulatory reviews), drafting project pitches, and comparing document drafts against historical precedents like scoping memos. 

### Which NLP technologies are they using?
The firm uses enterprise versions of large language models (Claude, ChatGPT, Gemini) hosted within a proprietary, secure browser-based environment to handle confidential client data safely. NotebookLM is used for summarizing large volumes of documents. Perplexity is utilized for public market research, and a specialized chatbot integrated into the beck-online database is tested for legal research. The firm also utilizes custom-developed internal AI tools for specific workflows, such as generating document markups.

### Stage
Production

### Challenges
* Lack of embedded AI applications within standard office software causes workflow friction.
* Poor performance and limited utility of standard tools like Microsoft Copilot for complex drafting tasks.
* Inability to seamlessly connect AI models to the firm's massive internal archives of historical transaction data.
* Risk of hallucinations and outdated legal knowledge, such as models failing to recognize legislative amendments.
* Context window limitations prevent the inclusion of extensive, multi-year transaction histories into prompts.
* AI-generated text often contains recognizable formatting and phrasing ("AI slop") that requires manual refinement.
* Difficulty conducting reliable legal research due to model cut-off dates and poor training on specialized German law.

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is an experienced M&A lawyer specializing in insurance transactions, working at the organization.

# Swiss Legal AI Software Provider

-------------

### Who is using it?
The primary users are courts and administrative bodies, particularly in Switzerland, that need to anonymize legal decisions for publication. The tool is also used by national authorities and public institutions (e.g., BfArM) that process documents under freedom of information principles. Smaller to mid-sized law firms show interest but are often constrained by budget and support requirements.

### What problem(s) are they trying to solve?
The provider addresses the resource-intensive and complex task of anonymizing Word and PDF documents. The goal is to ensure the reliable protection of personal data while maintaining the readability of legal texts through the use of placeholders instead of simple redaction (blacking out). Key challenges solved include reducing the manual effort of first-pass document reviews and avoiding technical pitfalls in PDFs where sensitive data might remain hidden beneath redaction layers.

### Which NLP technologies are they using?
The software utilizes specialized Named Entity Recognition (NER) models to identify candidates for anonymization, such as persons, organizations, and locations. Instead of large, cloud-based LLMs, the provider uses smaller, fine-tuned models that are deployed on-premise at the client's site for data security. The system combines these AI models with rule-based approaches like regular expressions (Regex) and pattern recognition. It is delivered as a Word Add-in that operates through a browser-based interface (Edge) within the application.

### Stage
Production

### Challenges
* High complexity in processing heterogenous PDF documents compared to Word files
* Risk of false positives and negatives, requiring a human-in-the-loop verification process
* Difficulty in identifying domain-specific terms (e.g., drug names or medical codes) without specialized training data
* Extremely long sales cycles of up to two years when dealing with public administrations
* General skepticism and fear of AI among staff, requiring extensive training and trust-building
* Technical debt in the public sector, with some clients still using outdated software like Word 2016
* Managing user expectations vs. reality, as some clients expect unrealistic 100% automation
* Growing "disillusionment wave" in the market where users become frustrated by AI's limitations
* Concerns regarding dependency on large infrastructure providers like Microsoft and their cloud security

Swiss Legal AI Software Provider

# Legal AI Usage in M&A by a German Legal Advisor

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### Who is using it?
An M&A specialist with over 20 years of experience, who recently transitioned from a Managing Partner role at a top 10 global law firm to a solo advisory practice. In the context of the global firm, users included partners, associates, and IT-savvy professional support staff who managed the technical processes and workflows.

### What problem(s) are they trying to solve?
The primary goal is to support complex M&A transactions and process massive volumes of data efficiently. Use cases include conducting legal due diligence by screening data rooms for specific conditions (e.g., non-compete clauses), summarizing extensive email threads within internal document management systems, and assisting in drafting standard contract elements like options agreements. Furthermore, AI is utilized for global knowledge management and CRM, allowing lawyers to query internal databases to identify past deal structures or locate specific industry expertise across the firm's global network.

### Which NLP technologies are they using?
The core technology utilized is a proprietary, internal AI system developed by the global firm, which automatically masks and anonymizes confidential client data before processing it through underlying external models. General-purpose models like ChatGPT are used occasionally for brainstorming or drafting simple explanations. Domain-specific AI tools, such as the assistant integrated into beck-online, are consulted for pure legal research. The interviewee also notes market awareness of contract analysis tools like SAP's AI features for benchmarking market standards.

### Stage
Production

### Challenges
* AI tools lack the strategic understanding and contextual awareness required to effectively structure highly bespoke M&A contracts.
* Models tend to overcomplicate drafts by inserting unnecessary clauses for obscure edge cases, making the resulting contracts excessively long.
* Complex interdependencies involving tax, balance sheets, and regulatory nuances are not reliably handled by current AI systems.
* AI struggles to grasp implicit context or historical connections in older email threads and unstructured legal documents.
* Strict client confidentiality mandates robust, resource-intensive data masking before interacting with any AI systems.
* Constant manual verification remains essential, as attorneys bear ultimate legal responsibility for the outputs and cannot rely blindly on the machine.

### Source
Interview led by sebis researchers. The interviewee is an M&A specialist and former Managing Partner of a global law firm, currently operating in a solo advisory capacity.

Legal AI Usage in M&A by a German Legal Advisor

# Legal AI Usage at German Law Firm

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### Who is using it?
Legal tech professionals, lawyers (ranging from associates to partners), young talents, and personal assistants (PAs) at a large German law firm. Specialized departments, such as the IT and investigations teams, also utilize these tools for specific mandates and large-scale document analysis.

### What problem(s) are they trying to solve?
The firm uses AI to automate and streamline daily legal and administrative tasks. Use cases include drafting and refining emails, summarizing lengthy communication threads, and generating contracts from scratch or via standard playbooks (e.g., filling investment agreements from cap tables). In the context of internal investigations and e-discovery, AI is used to process terabytes of data to extract specific communication events and build factual chronologies. They also utilize AI to automatically generate visual timelines for corporate transactions and are exploring ways to automate threshold notifications in antitrust law.

### Which NLP technologies are they using?
They primarily use Harvey (including its Word/Outlook add-ins and Harvey Vault) as their generalist AI tool for drafting, document analysis, and daily tasks. For specialized legal research involving commentaries, they are testing Beck nocs Tour, though it is currently viewed as linguistically weaker than global LLMs. They also use an internally developed IT tool for investigations and rely on AI-enhanced software like Structure Flow for data visualization. Other enterprise models, such as ChatGPT Enterprise and Anthropic's Claude, were previously evaluated in comparative tests.

### Stage
Production

### Challenges
* Inconsistent outputs and changing visual indicators when running AI playbook features for contract comparison
* Technical lag and performance limitations when analyzing massive datasets (e.g., 100,000 documents) within AI data rooms
* Strict licensing and copyright barriers that prevent the integration of high-quality legal databases (like Beck-online) into generalist LLM platforms
* Linguistic deficits and slower technological development in localized legal research tools compared to well-funded global LLMs
* Uneven user adoption across the firm, with some lawyers delegating their AI licenses and tasks to support staff
* Market noise and vendor overselling, where legacy tools are superficially rebranded with AI features that lack genuine utility
* The necessity to differentiate between workflows that actually require generative AI versus those better solved by traditional automation or machine learning

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is a lawyer specializing in legal technology, working at the organization.

# Legal AI Usage at German Law Firm

-------------

### Who is using it?
Lawyers across various levels of seniority, from young associates to partners, are utilizing legal AI tools. The users span multiple practice areas, including litigation, corporate law, and intellectual property (IP). A dedicated internal "Taskforce KI" actively monitors the market, evaluates tools, and guides adoption. Legal assistants also use general AI tools for administrative tasks and translation, though they do not have access to the specialized legal LLMs.

### What problem(s) are they trying to solve?
The firm uses AI to accelerate initial brainstorming, legal research, and document drafting (e.g., generating first drafts of GmbH articles of association or specific clauses). AI is also heavily used for document analysis, such as processing large volumes of annexes in court cases or conducting due diligence. Additional use cases include translating texts, correcting style, summarizing documents, creating structured visual outputs like timelines or tables, and even generating Python scripts to automate routine office workflows (e.g., filtering Outlook emails).

### Which NLP technologies are they using?
The firm employs a variety of cloud-based language models. Harvey is their primary tool for legal drafting and analysis because it meets their strict professional compliance requirements. ChatGPT is used for general, technical, and non-legal brainstorming, but strictly with anonymized data due to OpenAI's refusal to sign customized compliance declarations. For targeted legal research, they use database-linked tools like Juris KI Suite and beck-Noxx-Tool. Microsoft Copilot and DeepL Pro are used for office integration and translations. They are also preparing to introduce Google Gemini (specifically NotebookLM) because it satisfies their regulatory and confidentiality standards.

### Stage
Production

### Challenges
* Hallucinations and unreliable outputs in legal research requiring extensive manual verification
* Frustrating limitations with formatting and styling when using Microsoft Copilot in Word, Excel, and PowerPoint
* Strict data privacy and professional secrecy regulations restricting the use of popular tools like ChatGPT with real client data
* Significant cost increases for specialized legal database AI tools after initial trial phases end
* Inability of current models to reliably navigate and execute complex, multi-step formal procedures (e.g., cross-border service of process)
* Lingering skepticism and resistance from some senior partners who still view AI as "witchcraft"

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewees are a senior litigator and a junior associate, both working at the organization.

# Legal AI Usage at German Multinational Corporation

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### Who is using it?
In-house legal counsel advising the IT department of a large German multinational corporation. The users act as commercial and IT generalists, handling a high volume of daily requests, contract negotiations, and internal legal coordination. 

### What problem(s) are they trying to solve?
The primary goal is to increase efficiency in administrative and back-office tasks, such as sorting emails, finding documents, and drafting short communications. AI is highly valued as a feedback tool to simplify complex legal language into accessible internal corporate guidelines (e.g., directives on AI usage) for non-lawyers. While they are exploring AI for routine contract review (like NDAs) and tracking global legislative changes, they note that their core business—strategic contract negotiation and risk assessment—cannot yet be effectively automated. 

### Which NLP technologies are they using?
The legal team uses Microsoft Copilot for daily back-office support and Azure OpenAI for general experimentation. For domain-specific tasks, they have tested and are acquiring licenses for Beck NexTur, primarily for legal research and summarization. They also evaluate various third-party contract review tools and occasionally experiment with building internal prototypes, though they rely heavily on pre-existing, highly customized corporate templates rather than AI drafting.

### Stage
Production

### Challenges
* High reliance on specialized, company-specific templates and risk profiles limits the usefulness of AI drafting tools.
* Core daily tasks involve strategic negotiation and communication, which current AI models cannot replicate.
* Many commercial contract review tools are perceived as basic "AI wrappers" that do not integrate well into complex, iterative negotiation workflows.
* Poor cost-benefit ratio for specialized AI research tools, as their commercial IT roles require very little deep legal research.
* Internal clients (non-lawyers) frequently confront the legal team with confidently hallucinated AI legal advice or non-existent court rulings.
* Threat of budget pressures and job displacement if corporate management decides that "80% accurate" AI output is sufficient for certain tasks.

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewees are two in-house legal professionals working at the organization.

Legal AI Usage at German Multinational Corporation

# Legal AI Usage at Global Consulting Firm

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### Who is using it?
Legal technology consultants are using these tools to advise and enable in-house legal teams at corporate clients. The focus is on legal transformation, Legal Ops, and the implementation of Contract Lifecycle Management (CLM) systems for these corporate legal departments.

### What problem(s) are they trying to solve?
The primary goal is to digitalize and automate the contract lifecycle, including AI-assisted redlining, drafting, and extracting metadata from legacy contracts to populate CLM systems. Additionally, AI is heavily utilized for non-core legal and administrative tasks, such as chronologically summarizing massive, complex case files and verifying rule applications in documents (e.g., invoice audits). These administrative tasks yield the highest time savings, often reducing workload by up to 90% compared to strictly legal tasks.

### Which NLP technologies are they using?
They use AI capabilities integrated directly into specialized CLM platforms (such as Lia and Sirion). For generative and analytical tasks, they utilize a proprietary internal ChatGPT derivative, Microsoft Copilot, and Harvey, with which they hold an exclusive partnership. For deep legal research requiring specific German case law, they rely on specialized local databases like Beck Noctua, as general AI models lack access to this paywalled data.

### Stage
Production

### Challenges
* Difficulty in correctly matching the vast variety of available AI tools to specific, suitable use cases
* General LLMs lack access to paywalled, high-quality local German legal data, limiting their research capabilities
* Exclusive vendor partnerships occasionally restrict the adoption of competing tools that might be better optimized for civil law
* Mitigating hallucinations, although this is improving as newer models incorporate visible reasoning steps
* Users frequently lack adequate prompt engineering skills, requiring tools to automatically compensate for poor inputs

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is a legal technology consultant advising corporate in-house legal teams.

Legal AI Usage at Global Consulting Firm

# Legal AI Usage at International Law Firm

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### Who is using it?
Legal professionals, particularly within the patent law practice group, including knowledge lawyers, partners, and scientific assistants. Usage spans across demographics but is exceptionally high (around 90%) among younger employees.

### What problem(s) are they trying to solve?
The primary goals include managing internal case data, tracking deadlines, legal research, and drafting correspondence to overcome the "blank page" syndrome. A specialized use case in patent law involves summarizing and extracting knowledge from Unified Patent Court (UPC) decisions to build internal databases. Additionally, AI is used to quickly explain complex technical and scientific concepts (e.g., chemical formulas, telecommunications standards) required for patent litigation.

### Which NLP technologies are they using?
The firm relies heavily on Microsoft Copilot integrated into standard office applications (Word, Outlook, Power BI). They also use an internal proprietary AI assistant based on OpenAI's GPT models, developed via a joint venture. Legal publisher tools like Beck-online's NoxxTua and Beck Chat are currently in testing phases. For non-confidential, public data tasks, power users strongly prefer Anthropic's Claude models (specifically Opus) due to their advanced reasoning and tool-use capabilities. 

### Stage
Production

### Challenges
* Strict internal information security (InfoSec) requirements severely delay the procurement of new tools and block specialized startup solutions.
* Corporate preference for generalized tools (like Copilot) over specialized, niche legal AI tools.
* General lack of context engineering and prompting skills among lawyers, leading to unrealistic expectations of instant, perfect results from minimal input.
* Older or cost-optimized internal models struggling with context and hallucinating legal facts.
* Lack of deep integration with the firm's document management systems, preventing lawyers from querying entire case files at once.
* Fundamental conflict between AI-driven efficiency gains and the traditional billable hour business model.
* Long-term concerns about legal staff losing foundational analytical skills (the "Google Maps" effect) and shifting workforce dynamics.

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is a Patent Knowledge Lawyer, working at the organization.

# Legal AI Usage at German Law Firm

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### Who is using it?
Lawyers across various practice groups, including Tax, Private Clients, Corporate, and Regulatory, are using the tools. Partners are also increasingly driving adoption due to market awareness. A dedicated internal Innovation Lab consisting of 14 experts acts as a service provider to build, customize, and support these tools for both tech-savvy lawyers and broader firm use.

### What problem(s) are they trying to solve?
The most prominent use case is data extraction from legal and financial documents, such as tax assessments and compliance records. AI is also used for drafting support, particularly for emails, initial outlines for pleadings, and non-core legal tasks like preparing presentations or webinars. Furthermore, they are actively testing AI for document-heavy due diligence processes and using it for legal research, with future plans to apply AI to CRM and general law firm administration tasks.

### Which NLP technologies are they using?
They use self-hosted large language models (including GPT and LLaMa) accessed via an internal chatbot. The Innovation Lab develops complex expert tools using prompt chaining to produce structured data outputs (e.g., JSON). They also utilize AI modules natively integrated into standard legal databases like beck-online and Juris. Additionally, they have access to Microsoft Copilot and are currently running proofs of concept (POCs) for commercial legal AI tools such as Harvey, Legora, and Libra.

### Stage
Production

### Challenges
* Difficulty in measuring concrete efficiency gains and aligning them with the traditional billable hour model
* Extreme user attitudes ranging from overestimating AI's capabilities to completely dismissing it
* Lack of fully automated workflows; all AI outputs still require manual review and prompting refinement
* Licensing and technical barriers when trying to link internal tools with proprietary legal publisher databases
* Missing seamless integrations into daily workflows, particularly the need for reliable Word add-ins
* Ensuring strict data privacy and professional secrecy, especially when developing web search integrations
* Achieving widespread adoption for complex, high-value use cases beyond basic chatbot interactions

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is a legal professional working in the Innovation Lab of the organization, with a background in litigation.

# Legal AI Usage at German Legal Tech Software Provider

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### Who is using it?
Law firms (ranging from small practices to large commercial firms), corporate legal departments, and insolvency administrators. The end-users include lawyers, insolvency specialists, and back-office/secretarial staff handling administrative processes.

### What problem(s) are they trying to solve?
The provider aims to optimize both core legal work (contract drafting, review, and analysis) and repetitive administrative tasks (billing, document filing, controlling). In insolvency law, AI is used for text recognition, document summarization, and claims registration. The overarching goal is to integrate "Embedded AI" into existing firm management and document management systems to act as a digital backbone, thereby improving efficiency and mitigating the impacts of skilled labor shortages.

### Which NLP technologies are they using?
They use a model-agnostic approach, integrating various large language models including OpenAI (via Microsoft Azure), Claude (via AWS Bedrock), and Google Gemini, ensuring compliance with GDPR and professional secrecy laws. Customers can sometimes choose the underlying model. Different models are strategically used for specific steps, such as using one model for document review and another for drafting. They also employ Retrieval-Augmented Generation (RAG) and automated secondary validation loops (e.g., link checkers and secondary AI verification) to minimize hallucinations.

### Stage
Production

### Challenges
* Smaller law firms often lack the basic digitization and structured document storage required to effectively deploy AI tools.
* High variability in user adoption, requiring significant vendor investment in customer success and change management.
* Difficulty in establishing viable pricing models due to unpredictable token consumption and customer reluctance toward usage-based pricing.
* AI models struggle with drafting complex documents from scratch without access to proprietary legal publisher databases and templates.
* Managing hallucinations requires complex, multi-layered validation systems.
* Increasing threat of tech-savvy clients building their own custom tools using low-code/no-code platforms within their existing Microsoft ecosystems.
* Anticipated market consolidation and high customer acquisition costs threatening smaller Legal Tech startups.

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is a representative and in-house counsel (Syndikus) at a Legal Tech software provider.

Legal AI Usage at German Legal Tech Software Provider

# Legal AI Usage at German Law Firm

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### Who is using it?
Lawyers specializing in Venture Capital (VC) and M&A transactions at a German law firm are the primary users. Usage includes partners and associates, supported by an internal Tech & Automation team and in-house developers who build custom MVPs and prototypes.

### What problem(s) are they trying to solve?
The main goal is to automate the due diligence (DD) process, particularly for seed and pre-seed startups where review criteria are highly repetitive. They use AI to identify "red flags" or dealbreakers, perform semi-automated document reviews, and structure large volumes of data. Other use cases include checking the "Chain of Title" for capitalization history, summarizing negotiation notes into contract changes via Word add-ins, and analyzing market standards for VC investments through clause databases.

### Which NLP technologies are they using?
They primarily use Legora, a specialized legal automation technology, for tabular document reviews and due diligence. They also utilize Microsoft Copilot for broader tasks and have developed internal tools for managing document requests from founders. For software development and experimentation, tools like Claude are monitored, though data security remains a concern. The firm favors a "build" approach for specific processes while using cloud-based platforms for standard legal tech tasks.

### Stage
Production

### Challenges
* High manual effort required from founders to provide missing documents, which AI cannot yet solve
* Significant time investment needed to set up complex tabular reviews and automated workflows
* Low accuracy (60-80%) in simple prompting compared to higher accuracy (90-95%) in structured tabular tasks
* Billable hour model disincentivizes the time spent on IT system configuration and optimization
* Resistance or hesitation from some colleagues, including younger lawyers, to adopt new tools
* Initial frustration among users who use low-threshold, single-sentence prompts and receive poor results
* Difficulty in using AI for drafting from scratch due to lack of standard playbooks in specific legal areas
* Need for a complete mindset shift to design new AI-native processes rather than forcing AI into old workflows
* Strict security and data privacy requirements preventing the use of certain advanced coding and AI agents

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is a lawyer specializing in VC and M&A, working at the organization.

# Legal AI Usage at White & Case

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### Who is using it?
Lawyers and legal professionals at a large international law firm are the primary users. This includes senior partners, associates, and specialized tech transaction teams. Implementation is supported by a dedicated Chief Innovation Officer and an innovation team, alongside a global Tech Committee that manages the onboarding process for the firm's legal tech stack.

### What problem(s) are they trying to solve?
The firm aims to automate repetitive drafting tasks, improve document quality through automated proofreading (identifying inconsistencies and errors), and streamline the negotiation process. AI is used to compare incoming vendor terms against internal playbooks to identify acceptable fallback positions. Additionally, the tools help manage email correspondence related to contract updates and assist in the drafting of standard agreements, such as SaaS contracts, to save time and enhance consistency.

### Which NLP technologies are they using?
The firm utilizes a multi-layered approach including a proprietary chatbot hosted within their own Azure Cloud environment to ensure data security. They use Legora (integrated as a Word plugin and Outlook extension) for drafting and proofreading, and Vincent AI (vLex) for accessing public legal databases. The firm also employs various large language models (LLMs) via APIs and has explored Microsoft Copilot and Harvey. Strategy focuses on "Buy & Build," ensuring tools are integrated directly into existing workflows.

### Stage
Production

### Challenges
* High security and compliance requirements necessitate tools running exclusively within the firm's own environment.
* Significant change management effort is required to ensure lawyers actually adopt and trust the tools.
* Low technical affinity among many lawyers makes integration into existing workflows (Word/Outlook) critical for success.
* Risks of liability and malpractice cases arising from AI-generated errors or hallucinations.
* AI outputs for complex drafting tasks often lack the necessary quality ("garbage") without highly specific context and prompting.
* Market fragmentation and the focus of many vendors on the Anglo-American legal system rather than the German market.
* Fear and resistance among staff regarding the disruption of traditional legal work and the associate model.
* High costs of licensing and implementation do not necessarily lead to lower prices for clients due to increased overhead.
* Difficulty in maintaining "stickiness" and long-term routine usage after the initial rollout phase.

### Source
Interview conducted in March 2026, led by sebis researchers. The interviewee is an experienced lawyer and Of Counsel at the organization, with a long history in tech transactions and innovation committees.

Legal AI Usage at White & Case

# Legal AI Usage by a Specialist Lawyer in Commercial and Insolvency Law

-------------

### Who is using it?
A senior lawyer with nearly 30 years of experience, specializing in commercial, corporate, and insolvency-related law, is using legal AI. The firm’s internal IT department and a dedicated working group—including younger associates interested in legal tech—manage the evaluation and procurement of these tools. Usage intensity varies across the firm, with litigators and advisory teams using it more frequently than procedural departments.

### What problem(s) are they trying to solve?
The primary goals are to streamline the entry into legal research, summarize complex case facts, and generate initial drafts for legal statements or memos. AI is used as a "digital assistant" to provide a starting point for topics where the user is not the primary expert (e.g., labor law). Additionally, the firm aims to digitize administrative workflows, such as file creation and office management, to potentially reduce the need for administrative staff and increase overall efficiency.

### Which NLP technologies are they using?
The practitioner uses specialized German legal AI tools including Beck Noxtua, Juris (integrated AI features), and Beck Online Chat. These tools are used for document uploads, summarization, and research assistance. For private use and international or public law queries (e.g., international law), general-purpose models like ChatGPT are utilized. The workflow often involves using AI for an initial summary or "brainstorming" of points to consider before moving to traditional legal commentaries.

### Stage
Research

### Challenges
* High cost of specialized legal tools (e.g., Noxtua) relative to current performance.
* Inconsistent quality of legal reasoning, particularly in dogmatic fields like insolvency law compared to case-law-heavy fields like labor law.
* Frequent hallucinations or incorrect sourcing, such as providing obscure secondary sources instead of landmark High Court (BGH) decisions.
* AI currently performs at the level of a low-scoring law student rather than a qualified legal trainee (Referendar).
* Fragmented legal databases (e.g., Beck vs. Juris) prevent AI from accessing a comprehensive pool of legal knowledge.
* Difficulty in generating reliable complex legal drafts without significant manual correction.
* Lack of systematic understanding in AI models for fields requiring deep legal dogma and logical derivation.

### Source
Interview conducted in February 2026, led by sebis researchers. The interviewee is a senior lawyer and partner specializing in commercial and insolvency law.

Legal AI Usage by a Specialist Lawyer in Commercial and Insolvency Law

# Legal AI Usage at a German Law Firm and Legal Consulting Practice

-------------

### Who is using it?
The primary users are lawyers and legal engineers within a German law firm, as well as legal departments of large organizations (including DAX companies) that receive consulting on AI integration. The interviewee is a legal professional and legal engineer who develops custom AI solutions for various legal fields such as IT law, data protection, and procurement law.

### What problem(s) are they trying to solve?
The goals include massive efficiency gains in document-heavy processes. Key use cases involve contract review (e.g., verifying existing portfolios against new regulations like DORA), due diligence, and legal research in both public sources and specialized databases. Specific focus is placed on automating routine tasks like checking service charge statements for real estate portfolios and performing automated civil law assessments (BGB) to increase "Access to Justice" for small-scale legal claims.

### Which NLP technologies are they using?
They use a custom-built platform called "Digital Gateway Gen AI," based on Microsoft Azure, which integrates various LLMs including GPT-4, GPT-5.2 (experimental), Gemini, and Claude. They employ an "ecosystem of personas" (pre-configured agents) and are developing agentic systems where one orchestrator LLM controls other agents to perform complex, multi-step workflows. Microsoft Copilot is also used horizontally for standard office tasks, while specialized tools like Legora and Harvey are tested for vertical integration.

### Stage
Production

### Challenges
* High level of passivity among lawyers, with 80-90% of staff in large companies rarely using available AI tools
* Deep-seated fears regarding job security and professional identity leading to adoption resistance
* Significant knowledge gap in "AI literacy" and the technical skill required to build effective agents or prompts
* Lack of seamless integration between different software environments (e.g., moving data from chatbots to Word or Excel securely)
* Structural "blind spots" in organizations regarding how to digitize and structure institutional/implicit knowledge
* High licensing costs of specialized legal AI tools compared to general-purpose office integrations
* Need for strict human oversight (four-eyes principle) despite high model accuracy to manage liability
* Difficulty in shifting from a siloed "departmental" mindset to a holistic, process-oriented business approach

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is a practicing lawyer and legal engineer with nine years of professional experience, working at the organization.

Legal AI Usage at a German Law Firm and Legal Consulting Practice

# German Legal AI Software Provider

-------------

### Who is using it?
The tools are developed for a wide range of legal professionals, including lawyers in small family-owned firms, specialized boutique firms (e.g., in corporate or labor law), and in-house legal and procurement departments of large corporations. The provider also collaborates with major legal publishers (e.g., Lex Verlag) and integrates with standard office software like Microsoft Word and SharePoint.

### What problem(s) are they trying to solve?
The provider aims to reduce the time and effort required to create and finalize legal documents. Key use cases include automating the drafting of standard contracts (like employment agreements), providing "negotiation toolkits" for lawyers, and helping users who lack deep legal expertise (such as procurement staff) to work with standardized templates. The goal is to bridge the gap between "black box" AI and expert human knowledge by providing calibrated, non-opaque solutions.

### Which NLP technologies are they using?
The provider uses a multi-model approach, integrating various Large Language Models (LLMs) via APIs, including OpenAI (GPT-4), Google, and Mistral. They employ an internal benchmarking system to select the best-performing model for specific functions. For data-sensitive environments, they offer deployments on dedicated servers (e.g., via AWS or Microsoft Azure) to ensure compliance with professional secrecy and data protection standards. They also utilize "Transformer" models and explore hybrid approaches for template automation.

### Stage
Production

### Challenges
* Statistical nature of LLMs leading to results that are "best fits" rather than legally certain
* High effort and long lead times (often 2+ years) for corporations to structure and update internal templates
* Managing user expectations regarding "one-size-fits-all" AI solutions vs. specific legal expertise
* Rapidly changing model landscape requiring frequent re-evaluation (every 3-6 months) to maintain quality
* Difficulty in achieving 100% precision due to the inherent "hallucination" risks in generative models
* Compliance with strict professional secrecy laws when using cloud-based providers
* Risks of copyright or content infringement when AI heavily reformulates original legal texts
* Integration complexity with legacy document management systems (DMS) and internal IT infrastructures

### Source
Interview conducted in April 2026, led by sebis researchers.

# Legal AI Usage at German E-Commerce Company

-------------

### Who is using it?
The primary users are legal professionals within the Corporate Governance department, which includes legal counsel, compliance officers, internal auditors, and risk management staff. The department is a frontrunner in AI adoption within the company, with usage extending from associates to the General Counsel.

### What problem(s) are they trying to solve?
The organization aims to improve efficiency and resource allocation by automating low-complexity, repetitive tasks. Specific goals include summarizing large volumes of documents for internal audits, providing self-service legal advice via a policy chatbot, and conducting regional-specific legal research (e.g., a "Chinese Paralegal" assistant). They also work on automating the translation of standard operating procedures into visual process diagrams and performing regulatory horizon scanning to identify changes in legislation.

### Which NLP technologies are they using?
The firm is heavily integrated into the Google ecosystem, primarily using Gemini as their central productivity and generative AI tool. They utilize "Custom Gems" for tailored tasks and have experimented with Deep Research Models for niche legal questions. Additionally, they have recently integrated Noctua, a specialized legal AI tool, for tasks requiring high precision and data segregation from the internet. They also use automated coding assistants to create App Scripts for workflow automation within spreadsheets.

### Stage
Production

### Challenges
* Difficulty in quantifying exact efficiency gains despite high adoption rates
* Risks of "hallucinations" necessitating the use of tools that operate without internet connectivity
* High complexity of underlying manual processes making digital translation difficult
* Need to establish risk thresholds to determine when AI can act versus when a human must intervene
* Constant market pressure and difficulty in keeping pace with the rapid release of new AI tools and features
* Potential for stakeholders to receive "confirmatory" but incorrect advice from AI, leading to conflicts with legal counsel
* Requirement for intensive internal training (enablement) to move users beyond low-level task usage
* Ensuring that AI is viewed as a "means to an end" rather than the only solution for process optimization

### Source
Interview conducted in April 2026, led by sebis researchers. The interviewee is a legal professional responsible for AI rollout and digital transformation within the organization.

Legal AI Usage at German E-Commerce Company

# Legal AI Usage at a German Civil Court

-------------

### Who is using it?
A judge at a German civil court is using AI tools to assist with judicial tasks. While the judiciary is generally considered a "AI-free zone" due to a lack of specialized access, individual judges are beginning to test official pilots and use general-purpose models to support their work.

### What problem(s) are they trying to solve?
The judge aims to improve the quality and depth of judicial decisions rather than simply increasing speed. Key use cases include researching case law, summarizing case files, creating timelines of events from legal briefs, and identifying conflicting versus undisputed party claims. AI is also used as a "quality controller" to ensure all arguments have been addressed and to brainstorm legal interpretations or potential questions for witnesses and experts.

### Which NLP technologies are they using?
The primary tools used are Beck-Noxx (a specialized legal AI by Beck-Verlag currently in a pilot phase) and Claude (specifically the Opus 4.6 model). Because of strict data protection requirements, an anonymization tool developed by the University of Erlangen-Nuremberg (FAU) is used to redact personal data before documents are uploaded to AI interfaces. The judge also utilizes "Deep Research" functions for abstract legal problems and experiments with prompting to break down complex legal questions.

### Stage
Research

### Challenges
* Strict data protection and confidentiality requirements for judicial documents
* Necessity of manual anonymization before using AI tools, which adds significant time overhead
* Risk of "hallucinations" in specialized legal tools where cited decisions may not support the stated claim
* High technical barriers to exporting and processing documents from electronic case files (E-Akte)
* Concerns regarding "suggestion bias" and the impact of AI on judicial independence
* Societal and constitutional constraints that require a human judge to remain the sole decision-maker
* Commercial AI models lack access to legal literature hidden behind publisher paywalls
* The "human brain doesn't scale" problem: saving time on summaries doesn't replace the time needed to truly understand the case

### Source
Interview conducted in February 2026, led by sebis researchers. The interviewee is a judge working at a German civil court.

Legal AI Usage at a German Civil Court

Syntactic analysis, also known as parsing, involves analyzing the grammatical structure of sentences to understand relationships between words. The fundamental purpose of syntactic analysis is to derive meaning from a text. In the analysis, checks are in place so that texts which are not meaningful or which do not make sense can be rejected. The analysis is done at an in-depth level starting from the basic symbols to all the way to an entire sentence. The concept is of critical importance since it enables machines to understand human language. It plays an instrumental role in bridging the gap between humans and systems. Example NLP techniques for syntactic analysis include dependency parsing, construction of a syntax tree, or part-of-speech tagging.

Syntactic Analysis

Application programming interface (API) recommendation plays a crucial role in facilitating smart contract development by providing developers with a ranked list of candidate APIs for specific recommendation points. Deep learning-based approaches have shown promising results in this field. However, existing approaches mainly rely on token sequences or abstract syntax trees (ASTs) for learning recommendation point-related features, which may overlook the essential knowledge implied in the relations between or within statements and may include task-irrelevant components during feature learning. To address these limitations, we propose a novel code graph called pruned and augmented AST (pa-AST). Our approach enhances the AST by incorporating additional knowledge derived from the control and data flow relations between and within statements in the smart contract code. Through this augmentation, the pa-AST can better represent the semantic features of the code. Furthermore, we conduct AST pruning to eliminate task-irrelevant components based on the identified flow relations. This step helps mitigate the interference caused by these irrelevant parts during the model feature learning process. Additionally, we extract the API sequence surrounding the recommendation point to provide supplementary knowledge for the model learning. The experimental results demonstrate our proposed approach achieving an average mean reciprocal rank (MRR) of 68.02%, outperforming the baselines' performance. Furthermore, through ablation experiments, we explore the effectiveness of our proposed code representation approach. The results indicate that combining pa-AST with the API sequence yields improved performance compared with using them individually. Moreover, our AST augmentation and pruning techniques significantly contribute to the overall results. © 2024 John Wiley & Sons Ltd.

Application programming interface recommendation for smart contract using deep learning from augmented code representation

This work introduces an innovative system tailored for automated document processing and advanced question-answering. The dataset creation code employs PyMuPDF and NL TK, orchestrating a systematic extraction of textual information from PDF files. This methodical process extends to content tokenization, where the text is segmented into structured sentences, culminating in the creation of a Pandas data Frame. The resultant dataset is then stored in the Parquet format, ensuring both efficient retrieval and in-depth analysis capabilities. The chatbot component of the system integrates ChainLit and Haystack libraries, incorporating a BM25 retriever for precise document retrieval and a generative language model for nuanced natural language interactions. Proficient in handling user queries, the chatbot adeptly retrieves relevant documents from the meticulously curated dataset and generates responses marked by conciseness and contextual accuracy. This paper not only delineates the architecture and methodologies employed in the system but also explores the diverse applications of this integrated approach. Representing a significant advancement in intelligent document-based inquiries, this system showcases versatility across a spectrum of fields, underlining its potential impact in research, customer support, and various domains relying on automated information retrieval and interpretation. © 2024 IEEE.

Mining Mate: A Chat Bot for Navigating Mining Regulations Using LLM Models

With the rapid development of my country’s socialist market economy, the system of joint and several liability has been established in my country’s civil and commercial law and is playing an increasingly important role. There are also problems such as scattered regulations and contradictory laws and regulations at the level. Since there is no unified application principle established in judicial practice, the litigation burden caused by the recovery lawsuit also wastes a lot of trial resources. Dimensional key features distinguish confusing charges. Use regular expression technology to extract key content such as fact descriptions, defendants’ charges, relevant laws and regulations in legal documents and create JSON format documents; use stammer word segmentation and stop word list to remove stop words; use Word2Vec algorithm to represent text into vector form, establish a judicial judgment prediction model and an optimization model, and through experimental comparison, it is concluded that the performance of the model after adding focal loss is improved by 1.82%, 0.45%, 1.62%, and 1.62% compared with the cross entropy loss, and the final accuracy of the optimized model is 84.78%., the precision rate is 87%, the recall rate is 85%, and the F1 value is 85%. The system is expected to assist judicial workers in classifying crimes with joint liability and reduce the burden of judicial workers reading many legal documents to classify crimes. © 2024 the Author(s).

Problems and Effective Countermeasures in Joint and Several Liability of Civil and Commercial Law Based on Deep Learning Assessment

The study addresses the possibilities of using information technology and natural language in the study of legal norms. The study aims to develop methods for using artificial intelligence and natural language processing to analyse jurisprudence. To achieve this goal, automatic strategies were created to recognise the main topics in legal texts, identify key legal concepts and analyse the structure of documents. The results of the study include an analysis of existing methods of using technology and natural language to analyse legal norms. The methods used included machine and deep learning, syntactic and semantic analysis, an automated classification system, relative analytics, and decision prediction. In addition, new methods of analysing legal texts based on artificial intelligence and natural language processing were introduced. These methods include the use of a thematic model that automatically identifies the main themes in legal texts, as well as automatic detection of legal concepts, which identifies key concepts. In addition, neural networks are used to analyse the structure of legal documents, which allows for more accurate recognition and analysis of various structural elements in documents. Automatic text generation based on legal information and ways to classify legal texts was also introduced. Thus, the main results are the automation of the process of analysing and understanding legal texts, an increase in the efficiency and accuracy of identifying thematic patterns and key legal concepts, and improved accessibility and speed of legal information processing. The results obtained indicate a great potential for the use of technological tools in jurisprudence, which can significantly improve the quality and accessibility of legal services, contributing to more efficient resolution of legal issues. © The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).

Possibilities of using artificial intelligence and natural language processing to analyse legal norms and interpret them

The utilization of machine learning methods for the analysis and interpretation of legal documents has been growing over the years, yet their potential and limitations remain under-explored. This study aims to address this gap, using unsupervised machine learning techniques to discover legal concepts from a corpus of Spanish legal documents. In addition to striving for optimal results, our research also embarks on an exploration of the challenges and limitations of unsupervised machine learning, investigating its capabilities and limitations in legal text analysis. We demonstrate that even relatively simplistic methodologies can yield noteworthy insights, with the highest identification rate of 70% achieved by Topic Modeling with Latent Dirichlet Allocation (LDA). However, challenges were encountered with the identification of some concepts, suggesting potential improvements in the corpus preprocessing and tokenization stages or the techniques to be used. The findings underscore the potential of unsupervised learning algorithms in legal text analysis, offering an intriguing path for future research. While acknowledging the need for higher accuracy in practical applications, this study emphasizes the remarkable feat achieved and proposes a way forward for a hybrid or adaptable approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Exploring the Challenges and Limitations of Unsupervised Machine Learning Approaches in Legal Concepts Discovery

Judgments concerning animals have arisen across a variety of established practice areas. There is, however, no publicly available repository of judgments concerning the emerging practice area of animal protection law. This has hindered the identification of individual animal protection law judgments and comprehension of the scale of animal protection law made by courts. Thus, we detail the creation of an initial animal protection law repository using natural language processing and machine learning techniques. This involved domain expert classification of 500 judgments according to whether or not they were concerned with animal protection law. 400 of these judgments were used to train various models, each of which was used to predict the classification of the remaining 100 judgments. The predictions of each model were superior to a baseline measure intended to mimic current searching practice, with the best performing model being a support vector machine (SVM) approach that classified judgments according to term frequency—inverse document frequency (TF-IDF) values. Investigation of this model consisted of considering its most influential features and conducting an error analysis of all incorrectly predicted judgments. This showed the features indicative of animal protection law judgments to include terms such as ‘welfare’, ‘hunt’ and ‘cull’, and that incorrectly predicted judgments were often deemed marginal decisions by the domain expert. The TF-IDF SVM was then used to classify non-labelled judgments, resulting in an initial animal protection law repository. Inspection of this repository suggested that there were 175 animal protection judgments between January 2000 and December 2020 from the Privy Council, House of Lords, Supreme Court and upper England and Wales courts. © 2022, The Author(s).

Using machine learning to create a repository of judgments concerning a new practice area: a case study in animal protection law

Ethereum is a blockchain platform that allows developers to create smart contracts. Smart contracts are programs that can automatically execute and handle cryptocurrency funds. However, over a hundred thousand new smart contracts are deployed every day and inevitably contain vulnerabilities due to programming errors. Once deployed, smart contracts cannot be fixed or changed, leaving funds at risk. To mitigate it, we use deep learning to detect vulnerabilities in smart contracts. First, we create our own dataset of labeled smart contracts based on opcode sequences, since few smart contract codes and labeled datasets are publicly available. We collect opcode sequences by replaying real-world transactions from the Ethereum Mainnet in our fully synchronized node while we leverage a plugin called SODA"to label opcode sequences with vulnerability classes. Second, after data collection, we preprocess the data by removing duplicate opcode sequences, normalizing the sequences to the same length, and converting them into vectors. Finally, to detect vulnerabilities in smart contracts, we train a deep classification model using LSTM neural networks. Our model achieved an average accuracy of 82.63% and an F1-score of 79.74% across seven types of vulnerabilities, which is important for securing funds and logic in smart contracts. © 2023 IEEE."

Opcode Sequences-Based Smart Contract Vulnerabilities Detection Using Deep Learning

The acquisition of training data for AI modeling, especially in Natural Language Processing (NLP) and Question Answering (QA), is well-known as laborious and time-consuming process. However, the accessibility of such datasets is typically limited to specific domains and languages. In this paper, we introduce a framework designed for generating training data for such scenario, especially in the Vietnamese language and legal domain. Our approach only requires a source of document as input data, then it can generate a high quality dataset that can be used for model training from that data. We first extract named entities (NE) and part-of-speech (POS) components from these contexts, serves them as the basis for the QA pairs dataset. Subsequently, we transform these contexts into fill-in-the-blank (cloze) question formats and serve them as natural questions. Combining the answer and question pairs, we then construct a synthetic dataset. Finally, a simple filter was applied to eliminate the noise data from the synthetic dataset. In our experiments, the model using our method achieved a state-of-the-art performance on ALQAC dataset, a 68.52 in F1 score and 39.80 on exact match (EM) score. © 2023 IEEE.

VietLegalQA: Unsupervised Legal Question Answering for Vietnamese Using Cloze Translation Approach

In view of the existing traditional legal service, practitioners are unable to meet the huge demand; a large number of citizens are unable to determine the scope of the problems when they encounter infringement or require various legal assistance. Based on this, an automatic classification model of legal consultation based on Deep Learning is proposed in this paper. A KP+BiLSTM+Attention model is proposed. The Keyword Parser is introduced to extract key information. TF-IDF and part of speech tagging are used to filter out the important information in the user's legal problem description. The extracted keywords are given a weight value, and the other information weights are set to zero. The text information is transferred into two parallel word vector embedding layers. One of the word vector embedding layers transfers the results to the fusion layer for splicing, difference and point multiplication after the key information is converted into vector form. The output results are respectively connected with the results obtained from the other embedding layer as residuals. The final results are transferred to the BiLSTM+Attention model for training. The test results show that KP+BiLSTM+Attention model has significantly improved the accuracy and F1 value of the best benchmark method for text classification tasks of legal consulting. Therefore, KP+BiLSTM+Attention method has better performance in dealing with the classification of legal consulting issues. © 2023, Science and Information Organization. All Rights Reserved.

Research on the Text Classification of Legal Consultation Based on Deep Learning

The proposed work aims to develop a legal document summarization tool using abstractive summarization techniques, with a focus on creating informative and concise summaries of legal documents. Modern deep neural network based pretrained models such as Distilled Bi-directional and Auto-regressive Transformer(DistillBart), Legal Pegasus, and Legal Longformer Encoder-Decoder(LED) are used to deal with the problem of low training dataset availability. Various sentence ranking approaches such as keywords, Parts Of Speech(POS) tagging, chunking based approach, and the position of sentences in the document and sentence centrality based on similarity are used to deal with the issue of large size of legal judgements. To improve informativeness of summary, focus is on entities such as important dates, concerned people, and referred acts in the legal case. The performance of different models employed for summarization are evaluated using evaluation metrics such as Bilingual Evaluation Understudy(BLEU), Precision, Recall, F1 Score, and Recall Oriented Understudy for Gisting Evaluation(ROUGE). The proposed system is a valuable tool for legal professionals to quickly understand the main points of a legal document and make informed decisions.  © 2023 IEEE.

Legal Data Assistive Tool Using Deep-Learning

Recently, security issues of smart contracts are arising great attention due to the enormous financial loss caused by vulnerability attacks. There is an increasing need to detect similar codes for hunting vulnerability with the increase of critical security issues in smart contracts. Binary similarity detection that quantitatively measures the given code diffing has been widely adopted to facilitate critical security analysis. However, due to the difference between common programs and smart contract, such as diversity of bytecode generation and highly code homogeneity, directly adopting existing graph matching and machine learning based techniques to smart contracts suffers from low accuracy, poor scalability and the limitation of binary similarity on function level. Therefore, this paper investigates graph neural network to detect smart contract binary code similarity at the program level, where we conduct instruction-level normalization to reduce the noise code for smart contract pre-processing and construct contract control flow graphs to represent smart contracts. In particular, two improved Graph Convolutional Network (GCN) and Message Passing Neural Network (MPNN) models are explored to encode the contract graphs into quantitatively vectors, which can capture the semantic information and the program-wide control flow information with temporal orders. Then we can efficiently accomplish the similarity detection by measuring the distance between two targeted contract embeddings. To evaluate the effectiveness and efficient of our proposed method, extensive experiments are performed on two real-world datasets, i.e., smart contracts from Ethereum and Enterprise Operation System (EOS) blockchain-based platforms. The results show that our proposed approach outperforms three state-of-the-art methods by a large margin, achieving a great improvement up to 6.1% and 17.06% in accuracy. © 2023 Tech Science Press. All rights reserved.

Deep Learning-Based Program-Wide Binary Code Similarity for Smart Contracts

Precise extraction of the required activities from contract documents is crucial to develop a complete and accurate construction schedule. Currently, the scheduler is required to read the complete lengthy contract package to extract activities and convert them into a structured format to prepare a schedule. The current manual methods of activities extraction for schedule development are inefficient, laborious, and time-consuming. Although a few researchers have developed models for automated information extraction from legal documents, those models are applicable to quantitative requirements only. To address this, the current study proposes an automated information retrieval system to extract the activities provided in contracts using natural language processing (NLP) methods. The study implemented the dependency parsing technique that employed the syntactic features of requirement text to extract the actors, actions, objects, and conditions specified in the requirement. The model achieved an average recall and precision of 94% and 95%, respectively, on the requirements of a real design-build project. The proposed automated activities retrieval system is expected to help the schedulers develop the construction schedules. © 2022 ASCE.

Extraction of Activities Information from Construction Contracts Using Natural Language Processing (NLP) Methods to Support Scheduling

Legal information processing attracts attention from a number of organizations globally which includes research institutions; specific areas of interest ranges from representation of legal data, such as prior court cases, for countries which adopt common law system. These legal datasets typically include legislative acts or statutes, which vary from one country to another. Mining this legal data in order to extract useful information poses formidable challenges, which include coming up with ways for storing datasets, which are continuously generated and growing exponentially every year. Additional challenges include keeping up with amendments of the statutes and invalidating old statutes. This paper details a system architecture containing several key subsystems toward design and development of Legal Humanities for Africa, which is digital, query-able and easy to use and navigate by both lay user and experienced professionals. The Legal Humanities of Africa architecture has three modules, namely, knowledge base, knowledge engine and HCI module. The knowledge base handles the legal data dictionary, glossary and metadata in a domain-specific manner, while the knowledge engine handles processing data from the legal cases or statutes. Various approaches to computational linguistics, such as natural language processing or information extraction, have been used for natural language processing, including finding part of speech in text that contains most informative terms that are useful in computing similarity between prior cases to user queries. Submodules in knowledge base are also employed as needed to help optimize the process of extracting useful information to users usually in terms of relevant cases relating to specific legal matters at hand. Other techniques employed include several aspects of machine learning, such as unsupervised learning approaches to identify and cluster prior cases so that similar cases are clustered together, thus, making the process of matching user queries to prior cases easy. The HCI module provides user-specific (lay vs. experts), domain-specific and other anchor desks as required in a typical large application that can be commonly used by many types of users. A parametric evaluation of the performance of the Cloud-based Legal Architecture indicates that the system can enhance its use by both professionals and commoners; details are provided in this paper. It is hoped that the Legal Humanities of Africa architecture will become the benchmark architecture for Africa at large. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Deep Learning-Based Legal System Architecture for Africa: An Architectural Study

The nation's massive underground utility infrastructure must comply with a multitude of regulations. The regulatory compliance checking of underground utilities requires an objective and consistent interpretation of the regulations. However, utility regulations contain a variety of domain-specific terms and numerous spatial constraints regarding the location and clearance of underground utilities. It is challenging for the interpreters to understand both the domain and spatial semantics in utility regulations. To address the challenge, this paper adopts an ontology and rule-based Natural Language Processing (NLP) framework to automate the interpretation of utility regulations – the extraction of regulatory information and the subsequent transformation into logic clauses. Two new ontologies have been developed. The urban product ontology (UPO) is domain-specific to model domain concepts and capture domain semantics on top of heterogeneous terminologies in utility regulations. The spatial ontology (SO) consists of two layers of semantics – linguistic spatial expressions and formal spatial relations – for better understanding the spatial language in utility regulations. Pattern-matching rules defined on syntactic features (captured using common NLP techniques) and semantic features (captured using ontologies) were encoded for information extraction. The extracted information elements were then mapped to their semantic correspondences via ontologies and finally transformed into deontic logic (DL) clauses to achieve the semantic and logical formalization. The approach was tested on the spatial configuration-related requirements in utility accommodation policies. Results show it achieves a 98.2% precision and a 94.7% recall in information extraction, a 94.4% precision and a 90.1% recall in semantic formalization, and an 83% accuracy in logical formalization. © 2021 Elsevier Ltd

Ontology and rule-based natural language processing approach for interpreting textual regulations on underground utility infrastructure

The Legal Clinics (LC) are no-profit programs aimed to provide legal assistance to low-income citizens. Given the large number of consultations requested by the LC, the processes usually are delayed. This delay entails higher costs for the institutions that offer this legal advice. This paper explores the low-cost automation of legal problem identification in LC through the use of Natural Language Processing (NLP) techniques. We propose a methodology of text processing for the legal issue identification in the legal complaint assistance in LC. The method is based on preprocessing of the text, word to vector transformation, text identification models, and model evaluation for final classification. This methodology looks to accelerate the first step of the legal consultation process identifying the legal issue described by the LC user. The method is evaluated using real cases from the LC of the Santo Tomas University of Colombia. The results provided by the methodology depict a performance of around 95% for the legal issue identification. It is expected that this system will contribute to the delay decrease in the legal advice from LC, and it will help increase the number of advised users through virtual legal assistance. © 2021 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Automation system based on NLP for legal clinic assistance

Analysing judicial documents is a significant task in the field of legal intelligence, of which the prediction of law articles can help ordinary people better comprehend basic legal knowledge. It can be seen as a multi-label classification problem. Our work uses description of the cases as the input and views law articles as labels. After word segmentation and feature extraction, the classifier model TextCNN is used to train and predict the results. Results of comparative experiments show that our approach can achieve competitive results. © 2019 IEEE.

Law Article Prediction Based on Deep Learning

Legal texts express conditions in natural language describing what is permitted, forbidden or mandatory in the context they regulate. Despite the numerous approaches tackling the problem of moving from a natural language legal text to the respective set of machine-readable conditions, results are still unsatisfiable and it remains a major open challenge. In this paper, we propose a preliminary approach which combines different Natural Language Processing techniques towards the extraction of rules from legal documents. More precisely, we combine the linguistic information provided by WordNet together with a syntax-based extraction of rules from legal texts, and a logic-based extraction of dependencies between chunks of such texts. Such a combined approach leads to a powerful solution towards the extraction of machine-readable rules from legal documents. We evaluate the proposed approach over the Australian “Telecommunications consumer protections code”. © 2018, Springer Nature Switzerland AG.

Combining Natural Language Processing Approaches for Rule Extraction from Legal Documents

Query reformulation is the process of iteratively modifying a query to improve the quality of search engine results. In recent years, the task of reformulating natural language (NL) queries has received considerable diligence from both industry and academic communities. Traditionally, query reformulation has been mostly approached by using the noisy channel model. Since legal queries are diverse and multi-faceted, these traditional approaches cannot effectively handle low frequency and out-of-vocabulary (OOV) words. Motivated by these issues, we rethink the task of legal query reformulation as a type of monolingual neural machine translation (NMT) problem, where the input (source) query is potentially erroneous and the output (target) query is its corrected form. We propose a unified and principled framework with multiple levels of granularity. Specifically, (i) an encoder with character attention which augments the subword representation; (ii) a decoder with attentions that enable the representations from different levels of granularity to control the translation cooperatively and (iii) a semi-supervised methodology to extract and augment a large-scale dataset of NL query pairs combining syntactic and semantic operations. We establish the effectiveness of our methodology using an internal dataset, where the training data is automatically obtained from user query logs. We further demonstrate that training deep neural networks on additional data with synthesized errors can improve performance for translation. © 2019 Copyright held by the owner/author(s).

Legal query reformulation using deep learning

Currently, pest management practices require modern equipment and the use of complex information, such as regulations and guidelines. The complexity of regulations is the root cause of the emergence of automated solutions for compliance assessment by translating regulations into sets of machine-processable rules that can be run by specialized modules of farm management information systems (FMIS). However, the manual translation of rules is prohibitively costly, and therefore, this translation should be carried out with the support of artificial intelligence techniques. In this paper, we use the official Spanish phytosanitary products registry to empirically evaluate the performance of four popular machine learning algorithms in the task of correctly classifying pesticide regulations as prohibitions or obligations. Moreover, we also evaluate how to improve the performance of the algorithms in the preprocessing of the texts with natural language processing techniques. Finally, due to the specific characteristics of the texts found in pesticide regulations, resampling techniques are also evaluated. Experiments show that the combination of the machine learning algorithm Logic regression, the natural language technique part-of-speech tagging and the resampling technique Tomek links is the best performing approach, with an F1 score of 68.8%, a precision of 84.46% and a recall of 60%. The experimental results are promising, and they show that this approach can be applied to develop a computer-aided tool for transforming textual pesticide regulations into machine-processable rules. To the best of our knowledge, this is the first study that evaluates the use of artificial intelligence methods for the automatic translation of agricultural regulations into machine-processable representations. © 2018 Elsevier B.V.

Machine learning for automatic rule classification of agricultural regulations: A case study in Spain

In this work we present the TULSI system (so named after Turin University Legal Semantic Interpreter), a system to produce automatic annotations of normative documents through the extraction of modificatory provisions. TULSI relies on a deep syntactic analysis and a shallow semantic interpreter that are illustrated in detail. We report the results of an experimental evaluation of the system and discuss them, also suggesting future directions for further improvement. © 2012 Springer Science+Business Media B.V.

TULSI: An NLP system for extracting legal modificatory provisions

We describe a legal question answering system which combines legal information retrieval and textual entailment. We have evaluated our system using the data from the first competition on legal information extraction/ entailment (COLIEE) 2014. The competition focuses on two aspects of legal information processing related to answering yes/no questions from Japanese legal bar exams. The shared task consists of two phases: legal ad hoc information retrieval and textual entailment. The first phase requires the identification of Japan civil law articles relevant to a legal bar exam query. We have implemented two unsupervised baseline models (tf-idf and Latent Dirichlet Allocation (LDA)-based Information Retrieval (IR)), and a supervised model, Ranking SVM, for the task. The features of the model are a set of words, and scores of an article based on the corresponding baseline models. The results show that the Ranking SVM model nearly doubles the Mean Average Precision compared with both baseline models. The second phase is to answer “Yes” or “No” to previously unseen queries, by comparing the meanings of queries with relevant articles. The features used for phase two are syntactic/semantic similarities and identification of negation/antonym relations. The results show that our method, combined with rule-based model and the unsupervised model, outperforms the SVM-based supervised model. © Springer-Verlag Berlin Heidelberg 2015.

Legal question answering using ranking SVM and syntactic/semantic similarity

Previous work has shown that modeling relationships between articles of a regulation as vertices of a graph network works twice as better than traditional information retrieval systems for returning articles relevant to the question. In this work we experiment by using natural language techniques such as lemmatizing and using manual and automatic thesauri for improving question based document retrieval. For the construction of the graph, we follow the approach of representing the set of all the articles as a graph; the question is split in two parts, and each of them is added as part of the graph. Then several paths are constructed from part A of the question to part B, so that the shortest path contains the relevant articles to the question. We evaluate our method comparing the answers given by a traditional information retrieval system - vector space model adjusted for article retrieval, instead of document retrieval - and the answers to 21 questions given manually by the general lawyer of the National Polytechnic Institute, based on 25 different regulations (academy regulation, scholarships regulation, postgraduate studies regulation, etc.); with the answer of our system based on the same set of regulations. We found that lemmatizing increases performance in around 10%, while the use of thesaurus has a low impact. © Springer-Verlag Berlin Heidelberg 2009.

NLP for shallow question answering of legal documents using graphs

Ongoing research in information retrieval offers new approaches demonstrating that the traditional methods may have reached their limitations. In fact, actual results to date have shown that advanced systems will demonstrate effectiveness at combining multiple text and user's need representations. This paper suggests to supplement the traditional conceptual abstracts by a syntactical description designed to improve retrieval performance. This choice is carefully motivated and examples are given. Without attempting to reason about the documents content, a domain-specific knowledge representation is used to form clusters of concepts. The point is to represent the joint structure of the component-concepts involved in a composite-concept. This approach extends the reach of key concepts assignment to: interactive query elicitation; multiwords phrase search; and finer-grained thesaurus relationships. This paper emphasizes that intellectual assignment of features is part of artificial intelligence approach to information retrieval. The principles of implementation are also described.

Contribution to indexing in legal information retrieval

The complexity and diversity of government regulations make understanding the regulations a non-trivial task. One of the issues is the existence of multiple sources of regulations and interpretive guides; the latter are often independent of governing bodies. This work aims to develop an information infrastructure for legal information retrieval with applications to electronic-rulemaking. The pilot study focuses on accessibility regulations from the US Federal government, private organizations and European agencies. A shallow parser is developed to consolidate different regulations into a unified XML format, which is well suited for handling semi-structured data such as legal documents. Handcrafted rules and a text mining tool are developed to extract the important features, such as concepts, measurements, effective dates and so on, and to incorporate them into the corpus.To compare and locate related provisions from different regulatory documents, we employ Information Retrieval techniques to combine generic features with domain knowledge. Structural information from regulations, such as the hierarchical organization of provisions and heavy referencing among provisions, are used to help improve the relatedness analysis. Results are obtained to illustrate the use of regulatory structure and domain knowledge in provision comparisons. Application to an e-rulemaking scenario for a rights-of-way drafted regulation is shown to demonstrate extended capabilities of the prototype system. Copyright 2005 ACM.

Legal information retrieval and application to E-rulemaking

This paper introduces GENTRAC, an open-access web-based tool built to interactively detect and analyze potentially traumatic content in witness statements of genocide and mass atrocity trials. Harnessing recent developments in natural language processing (NLP) to detect trauma, GENTRAC processes and formats court transcripts for NLP analysis through a sophisticated parsing algorithm and detects the likelihood of traumatic content for each speaker segment. The tool visualizes the density of such content throughout a trial day and provides statistics on the overall amount of traumatic content and speaker distribution. Capable of processing transcripts from four prominent international criminal courts, including the International Criminal Court (ICC), GENTRAC's reach is vast, tailored to handle millions of pages of documents from past and future trials. Detecting potentially re-traumatizing examination methods can enhance the development of trauma-informed legal procedures. GENTRAC also serves as a reliable resource for legal, human rights, and other professionals, aiding their comprehension of mass atrocities' emotional toll on survivors.

GENTRAC: A Tool for Tracing Trauma in Genocide and Mass Atrocity Court Transcripts

This study aims to tackle some challenges posed by legal texts in the field of NLP. The LegalEval challenge proposes three tasks, based on Indial Legal documents: Rhetorical Roles Prediction, Legal Named Entity Recognition, and Court Judgement Prediction with Explanation. Our work focuses on the first two tasks. For the first task we present a context-aware approach to enhance sentence information. With the help of this approach, the classification model utilizing InLegalBert as a transformer achieved 81.12% Micro-F1. For the second task we present a NER approach to extract and classify entities like names of petitioner, respondent, court or statute of a given document. The model utilizing XLNet as transformer and a dependency parser on top achieved 87.43% Macro-F1.

TeamUnibo at SemEval-2023 Task 6: A transformer based approach to Rhetorical Roles prediction and NER in Legal Texts

We present GENTLE, a new mixed-genre English challenge corpus totaling 17K tokens and consisting of 8 unusual text types for out-of-domain evaluation: dictionary entries, esports commentaries, legal documents, medical notes, poetry, mathematical proofs, syllabuses, and threat letters. GENTLE is manually annotated for a variety of popular NLP tasks, including syntactic dependency parsing, entity recognition, coreference resolution, and discourse parsing. We evaluate state-of-the-art NLP systems on GENTLE and find severe degradation for at least some genres in their performance on all tasks, which indicates GENTLE's utility as an evaluation dataset for NLP systems.

GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation

Legal-ES is an open source resource kit for legal Spanish. It consists of a large scale Spanish corpus of open legal texts and different kinds of language models including word embeddings and topic models. The corpus includes over 1000 million words covering a collection of legislative and administrative open access documents in Spanish from different sources representing international, national and regional entities. The corpus is pre-processed and tokenized using Spacy. For the word embeddings, gensim was used on the collection of tokens, producing a representation space that is especially suited to reflect the inherent characteristics of the legal domain. We calculate also topic models to obtain a convenient tool to understand the main topics in the corpus and to navigate through the documents exploiting the semantic similarity among documents. We will analyse the time structure of a dynamic topic model to infer changes in the legal production of Spanish jurisdiction that have occurred over the analysed time framework.

Legal-ES: A Set of Large Scale Resources for Spanish Legal Text Processing

In this work we present a corpus for the evaluation of sensitive information detection approaches that addresses the need for real world sensitive information for empirical studies. Our sentence corpus contains different notions of complex sensitive information that correspond to different aspects of concern in a current trial of the Monsanto company. This paper describes the annotations process, where we both employ human annotators and furthermore create automatically inferred labels regarding technical, legal and informal communication within and with employees of Monsanto, drawing on a classification of documents by lawyers involved in the Monsanto court case. We release corpus of high quality sentences and parse trees with these two types of labels on sentence level. We characterize the sensitive information via several representative sensitive information detection models, in particular both keyword-based (n-gram) approaches and recent deep learning models, namely, recurrent neural networks (LSTM) and recursive neural networks (RecNN). Data and code are made publicly available.

A Real-World Data Resource of Complex Sensitive Sentences Based on Documents from the Monsanto Trial

This article presents the current outcomes of the MARCELL CEF Telecom project aiming to collect and deeply annotate a large comparable corpus of legal documents. The MARCELL corpus includes 7 monolingual sub-corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing the total body of respective national legislative documents. These sub-corpora are automatically sentence split, tokenized, lemmatized and morphologically and syntactically annotated. The monolingual sub-corpora are complemented by a thematically related parallel corpus (Croatian-English). The metadata and the annotations are uniformly provided for each language specific sub-corpus. Besides the standard morphosyntactic analysis plus named entity and dependency annotation, the corpus is enriched with the IATE and EUROVOC labels. The file format is CoNLL-U Plus Format, containing the ten columns specific to the CoNLL-U format and four extra columns specific to our corpora. The MARCELL corpora represents a rich and valuable source for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.

The MARCELL Legislative Corpus

The complexity and diversity of government regulations make understanding the regulations a non-trivial task. One of the issues is the existence of multiple sources of regulations and interpretive guides; the latter are often independent of governing bodies. This work aims to develop an information infrastructure for legal information retrieval with applications to electronic-rulemaking. The pilot study focuses on accessibility regulations from the US Federal government, private organizations and European agencies. A shallow parser is developed to consolidate different regulations into a unified XML format, which is well suited for handling semi-structured data such as legal documents. Handcrafted rules and a text mining tool are developed to extract the important features, such as concepts, measurements, effective dates and so on, and to incorporate them into the corpus.To compare and locate related provisions from different regulatory documents, we employ Information Retrieval techniques to combine generic features with domain knowledge. Structural information from regulations, such as the hierarchical organization of provisions and heavy referencing among provisions, are used to help improve the relatedness analysis. Results are obtained to illustrate the use of regulatory structure and domain knowledge in provision comparisons. Application to an e-rulemaking scenario for a rights-of-way drafted regulation is shown to demonstrate extended capabilities of the prototype system.

Legal information retrieval and application to e-rulemaking

The present work proposes a legal assistance tool based on NLP and a predictive ML model adapted to the particularities of Colombian Justice system for the Legal Defense Agency of the Colombian State (ANDJE), It is developed in 4 parts associated with specific requirements: An Exploratory Data Analysis of the available information, an NLP pre-processing of large volumes of information structured in databases and not structured in scanned PDFs, development of predictive models and user assistance tool based in Transformers. Combining in an original and innovative way predictive, information presentation, similarity searching engine and summary, Question Answering modules for treatment of large legal texts, to increase the probability of success in defending the interests of the Colombian state. Considerations for future works.

Assistance System for Judicial Awards for The Colombian State Legal Defense Agency Through NLP -ANDJE-

Ongoing research in information retrieval offers new approaches, demonstrating that the traditional methods may have reached their limitations. In fact, actual results to date have shown that advanced systems will demonstrate effectiveness at combining multiple text and user need representations. The paper suggests supplementing the traditional conceptual abstracts with a syntactical description designed to improve retrieval performance. This choice is carefully motivated and examples are given. Without attempting to reason about the document contents, a domain specific knowledge representation is used to form clusters of concepts. The point is to represent the joint structure of the component concepts involved in a composite concept. This approach extends the reach of key concept assignment to: interactive query elicitation; multiword phrase search; finer grained thesaurus relationships. The paper emphasizes that intellectual assignment of features is part of an artificial intelligence approach to information retrieval. The principles of implementation are also described.

A contribution to indexing in legal information retrieval

This paper describes an information extraction system that identifies and analyzes statements in court opinions regarding the value of cited cases as precedents. The system employs partial parsing techniques in conjunction with a semantic grammar to identify the language associated with such rulings. The most novel aspect of the system lies in its anaphora resolution module that combines syntactic, semantic, and domain-specific inference rules with local discourse information to link such language to case references.

Anaphora resolution in the extraction of treatment history language from court opinions by partial parsing

Aide (`AustLII Inferencing Development Environment') provides a quasi-natural language form of knowledge representation which is reasonably close to statutory language, but at the same time represents knowledge so that it can be used by an inferencing engine using predicate calculus.Aide also provides a supportive development environment for the user by parse tree differentiation to assist the user choosing the correct parsing of rules. The development environment and the user environment are both web-based, facilitating collaborative development of knowledgebases, and the integration of inferencing dialogues with legal source texts on the web.

AustLll's aide — natural language legislative rulebases

The Dutch Tax and Customs Administration (DTCA) is one of many organizations that deal with a multitude of electronic legal data, from various sources and in different formats. In this paper, we describe the results of a study aimed at better access to these sources by having a supplier and format independent knowledge store that describes the sources and their interrelations in a semantic network. Furthermore we developed parsers to automatically detect the identity of sources and typed references within the sources to other legal documents. These parsers can be used to fill and update the semantic network as new documents are added.

Constructing a semantic network for legal content

Text classification is an important task in the legal domain. In fact, most of the legal information is stored as text in a quite unstructured format and it is important to be able to automatically classify these texts into a predefined set of concepts.Support Vector Machines (SVM), a machine learning algorithm, has shown to be a good classifier for text bases [12]. In this paper, SVMs are applied to the classification of European Portuguese legal texts - the Portuguese Attorney General's Office Decisions - and the relevance of linguistic information in this domain, namely lemmatisation and part-of-speech tags, is evaluated.The obtained results show that some linguistic information (namely, lemmatisation and the part-of-speech tags) can be successfully used to improve the classification results and, simultaneously, to decrease the number of features needed by the learning algorithm.

Is linguistic information relevant for the classification of legal texts?

One of the main obstacles to progress in the field of artificial intelligence and law is the natural language barrier, but the technology of natural language processing has advanced recently. In this paper, we will show that a state-of-the-art statistical parser can handle even the complex syntactic constructions of an appellate court judge, and that a deep semantic interpretation of the full text of a judicial opinion can be computed automatically from the output of the parser. Our ultimate goal is to use this semantic interpretation to extract from a judicial opinion precisely the information that a lawyer wants to know about a case.

Deep semantic interpretations of legal texts

The ultimate goal of the research line described here is support for automated modelling of sources of law. One of the first steps is the automatic recognition of norms. In earlier work we presented a categorization of norms or provisions in legislation. We claimed that the categories are characterized by the use of typical sentence structures and that this would enable automatic detection and classification. In this paper we present the results of experiments in such automatic classification of provisions. We have defined fourteen different categories of provisions, and compiled a list of 88 sentence structures for those categories from twenty Dutch laws. Based on these structures, a parser was used to classify the sentences in fifteen different Dutch laws, classifying 91% of 592 sentences correctly. It compares well with other, statistical approaches. An important improvement of our classifier will be the distinction of principal and auxiliary sentences.