Transforming Legal Research with AI Legal Text Analysis

The Center for Public Health Law Research (CPHLR) at Temple University’s Beasley School of Law faced a significant challenge: the scientific legal mapping process of legal epidemiology is critical to their work, but the traditional tools for organizing and analyzing complex legal texts were inefficient. Enter MonQcle – a software tool developed and maintained by CPHLR was designed to support the creation of quantitative legal datasets and the publication of scientific legal mapping products.

In 2022, the CPHLR envisioned developing an AI legal assistant to enhance the MonQcle platform, allowing researchers to further streamline their scientific mapping workflow and reduce the time-intensive burden of legal coding.

To achieve this, CPHLR turned to Winder.AI for AI development expertise.

The Solution: AI Legal Assistant

Winder.AI collaborated with CPHLR to develop a legal AI Assistant capable of analyzing vast amounts of legal text, ingesting a large corpus of legal research data, and providing answers to coding questions that are drafted to objectively measure the law.

The AI Assistant is designed to:

• Ingest and Analyze Legal Data: Analyze a corpus of legal texts to automatically identify relevant sections of law that could help to answer specific coding questions
• Streamline the Workflow: Once fully integrated into MonQcle, the AI Assistant has the power to significantly cut down the time required for legal coding
• Enable Future Workflows: A future version of the AI assistant will be able to fully automate the coding process, answering the researchers questions on large corpora in an instant.

This collaboration resulted in a first-generation AI Assistant that will transform the MonQcle platform, bringing scientific legal mapping into the modern era.

The Results: Efficiency and Accuracy in Legal Coding

By integrating legal AI into the MonQcle platform, CPHLR aims to transform the way legal coding is conducted. The AI legal Assistant has the potential to drastically reduce the time required to complete legal coding, enabling researchers to focus on higher-value tasks and make faster, data-driven decisions.

The solution substantially speeds up scientific legal mapping for CPHLR which leads to:

• Larger datasets: The AI Assistant’s ability to work on large datasets will not only streamline the work of the CPHLR team but has the potential to benefit the entire field of scientific legal mapping.
• Scalability: Speed brings new levels of scalability, allowing researchers to do more faster than they ever have before.

Client Testimonial

The AI Assistant has the potential to change the day-to-day workflow and effort of not only our team, but the field at large. Winder.AI dug into our day-to-day workflow to thoroughly understand the value of an AI Assistant for scientific legal mapping, which is a critical process to the field of legal epidemiology. Their team was a pleasure to work with—professional, bright, communicative, adaptable, and always responsive to our shifting needs and timelines.

Lindsay K. Cloud, Deputy Director, Center for Public Health Law Research at Temple University’s Beasley School of Law

Why Winder.AI?

Winder.AI’s approach to the project went beyond technical expertise. They immersed themselves in the nuances of scientific legal mapping, worked closely with diverse stakeholders, and maintained open communication throughout the project. Their ability to adapt and deliver a solution that not only met but exceeded client expectations solidified their position as trusted AI experts.

Key Highlights:

• Deep understanding of legal mapping processes
• Highly professional and adaptable team
• Significant improvements in research efficiency and accuracy

If you’re looking for AI solutions to transform complex workflows and enhance productivity, Winder.AI is the partner you need. Contact us to discuss your requirements.