Legal firms were drowning in case data. Researching precedents, reading through verdicts, and trying to estimate win probabilities was slow, inconsistent, and heavily dependent on individual experience. They needed something that could absorb that volume and give lawyers actual analytical leverage, not just a faster search bar.
Challenges Identified:
- Research was eating the calendar: Associates were spending days on tasks that should take hours, cross-referencing case law, reviewing verdicts, pulling context from fragmented sources across multiple internal systems.
- Search tools didn’t understand legal language: Keyword-based search missed the point. A query about negligence in product liability would return anything with those words, not the cases that actually shaped the legal argument at hand.
- Win estimates were informal: Partners relied on experience and gut instinct to assess case probability. That worked to a point, but it couldn’t be documented, calibrated, or shared consistently across the team.
- Data sat in silos: Case histories, verdict records, and legal documents were spread across multiple systems with no unified way to query them together.
Solution Features:
- Language understanding via OpenAI API and LLaMA: Rather than surface-level text matching, these models interpret the legal meaning of documents, identifying relevant arguments, reading case context, and reasoning through nuanced legal language the way a trained associate would.
- Precedent retrieval via OpenAI Embeddings: A contextual similarity algorithm finds historically relevant cases based on the substance of the legal argument, not just shared vocabulary. Lawyers started surfacing cases they hadn’t been finding with traditional search.
- Distribution, based probability modelling: Rather than a single win/lose confidence score (which tends to be gamed or dismissed), we built a model that shows the range of likely outcomes under different conditions. Partners found this more honest and more useful for resource allocation decisions.
- End-to-end platform: Analysis, retrieval, and probability scoring are wired together in a React, Java, and Python stack backed by MySQL and MongoDB, so lawyers work in one place rather than jumping between tools.
Advantages:
- Research time down sharply: Tasks that previously occupied a full day now take a fraction of the time, freeing the team to focus on argumentation and strategy rather than data gathering.
- More relevant precedents: The contextual retrieval model surfaces cases the team wasn’t finding before, including some that materially shifted strategy on active matters.
- Probability outputs are now part of the workflow: Partners use the scoring model in case review meetings to guide decisions on where to deploy senior time, what to settle, and what to fight.
- Scales without adding headcount: As case volume grows, the platform absorbs more without proportionally more research effort.