June 6, 2023

Interview with Matt Francis – Co-Founder of Pattern Data

Author: Pawan Murthy

We sat down with Matt Francis, CEO and co-founder of Pattern Data, to ask him what inspired him and his co-founder James Nix to launch this company.

Q. When did you start Pattern Data, and what was your inspiration? 

A. We started Pattern Data at the beginning of 2020 amidst the Covid pandemic. My team and I had prior experience working as Professional Services in the back office for law firms and as a Claims Administrator. We noticed that law firms were struggling to develop and manage large volumes of claimants without sufficient information on the quality of those cases. We had experienced this issue firsthand between 2011 to 2013 while administering large complex torts like the World Trade Center. At that time, there was no opportunity to use technology to efficiently solve the problem, as the infrastructure, quality of OCR, and capability to train models effectively simply did not exist.

I was interested in finding a technology-based solution. However, the technical infrastructure for doing so was not available. Fast forward to before we started the business, and we engaged in R&D for the better part of 12 months on sample cases provided by a friendly firm (anonymized, of course). We found that we could review all of these cases and medical records using AI. We developed the technology for over a year to ensure high enough accuracy, which was crucial for our solution.

Q. What are some of the unique challenges that face law firms who work with Mass Torts? 

A. There are many challenges involved in managing a large volume of clients across the entire operational workflow, especially in the mass torts practice area. Engaging clients, obtaining necessary prerequisites, and collecting information required on those cases are just some of the challenges. That’s why our company is focused on one of the most time and labor intensive processes in mass torts: reviewing medical records. In most firms, individuals are tasked with opening up PDFs and reading through them, potentially using “control-f” search for specific key terms. This process is time-consuming, expensive, and the information is not saved in a way that’s usable for analytics purposes or downstream processes. 

Q. How do law firms deal with this backlog of work? 

A. For the longest time, law firms would either need to staff up or find someone to outsource the task, which can be challenging. Often, vendors don’t process the cases correctly, and firms end up being inefficient or leaving money on the table. At Pattern Data, our goal is to help law firms always be prepared, so they don’t have to choose one task or one tort over another. We offer a solution that allows firms to leverage the data they aggregate and use it for multiple purposes. 

Q. The legal industry has been wary of its reliance on AI. How does Pattern Data address those concerns? 

A. Effective use of AI in the legal space, particularly in mass torts, cannot be achieved without involving human expertise at a fundamental level. One of the biggest challenges with many AI applications is the lack of transparency in how the software’s decisions are being reached. At Pattern Data, we don’t produce AI results in a black box. We place great importance on the human element and the validation of results. Legal teams can get the benefit of predictive analytics using trained models specific to their tort, as well as the added assurance from having their team validate the results of the platform. Over time, our platform learns from this validation and the results improve over time. Our priority when starting Pattern Data as a technology business in the legal space was to build a user-friendly software platform that has consumer-grade interfaces. It needs to be incredibly user-focused, and intuitive. We designed Pattern Data to complement the expertise and experience of everyone in the legal team.

Facebook Twitter LinkedIn

back to all news

Request a Demo.

Interested in learning more? Fill out the form to request a demo.