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The honest origin story: I'm a litigator, and I built this out of frustration with my own job. Deposition review is some of the most important work in a case — it's where the contradictions live, where credibility gets made or broken — and I was doing it by hand. Highlighters, legal pads, sticky tabs, re-reading the same 200 pages to find the one line where a witness changed their story. Nights and weekends gone. It wasn't just inefficient; it was the kind of grind that burns people out of this profession.
So the problem I set out to solve was narrow at first: summarize the transcript faster. Get me a clean, cited summary so I'm not starting from a blank page.
But the deeper I got, the more I realized a summary wasn't the actual job. A summary tells you what's in one deposition. What I needed was to understand the case — how three witnesses' accounts line up, where the credibility is shaky, what the evidence says about liability and damages. That's when the approach shifted from "AI summarizer" to a structured evidence model: organize every transcript the same way (timeline, credibility, key testimony, legal issues, liability & damages), then let that structure power cross-witness conflict analysis and case valuation. The summary became the front door, not the product.
The thing I most believe: AI shouldn't replace legal judgment. It should hand you the record, already organized, so you can spend your time thinking instead of hunting.
Would love your feedback — I'm in the comments all day.

LawLM.ai

Evidence Intelligence for Complex Litigation

Evidence Intelligence for Complex Litigation