Company 

Morgan & Morgan operates as America's largest personal injury law firm with over 1,000 attorneys across all 50 states, handling high-volume caseloads through a contingency fee model that removes upfront costs for clients. The firm processes everything from auto accidents to mass torts at unprecedented scale, leveraging their size to take on major corporations and insurance companies that smaller firms can't match.

The Challenge

With their massive scale came a crushing data problem: Morgan & Morgan's attorneys were drowning in millions of documents across tens of thousands of cases, relying on manual review processes that hadn't evolved in decades. Each case required manual classification and summarization of documents, while valuable patterns from past case outcomes remained locked in unstructured data that couldn't inform future decisions. Their size—typically their advantage—threatened to become a liability as competitors modernized their tech stacks and Morgan & Morgan risked being stuck with legacy systems that couldn't keep pace. They needed teams that could work within their existing framework while delivering Silicon Valley-level innovation speed, not traditional consulting firms with 18-month roadmaps.

The Solution

Morgan & Morgan partnered with A.Team to embed builders with deep learning and ML expertise directly under their CTO and Head of Engineering. The builders developed machine learning models to optimize legal case analysis by identifying similar legal matters and predicting potential outcomes, creating an innovative weighting logic for each case component. Simultaneously, ML engineers built NLP models to automate the time-consuming process of manually classifying, summarizing, and extracting entities from massive troves of legal documents. The initial engagement proved so valuable that Morgan & Morgan scaled from four to nine A.Team builders, expanding the team with skilled data scientists, front-end developers, and backend engineers. This larger team went beyond the initial AI models to develop a client portal, establish a machine learning model for case valuation, create a centralized knowledge platform, and revamp the overall customer experience—fundamentally transforming how the firm leverages its vast data resources.