Has anyone else tried using Humata for legal document analysis, specifically for contracts? I've been running it through its paces over the last few weeks, comparing its output to manual reviews, and I'm hitting some interesting—and frankly, concerning—trade-offs.
On the speed front, it's undeniably fast. Uploading a batch of PDF lease agreements and asking for summaries, key clauses, and potential risks generates answers in seconds. For a first-pass, high-level triage, that's powerful. It feels like having a supercharged, always-on paralegal for initial screening.
But the accuracy... that's where my data-engineer brain gets twitchy. I've noticed a pattern:
* **Ambiguous clause interpretation:** It often presents a "potential risk" as a definitive statement. For example, a standard force majeure clause was flagged as "may allow unlimited delays," which is an over-simplification of the actual language.
* **Citation slippage:** This is my biggest gripe. When asked "What are the termination rights in Section 8?", it sometimes pulls text from *near* Section 8, or blends language from different sub-sections. In a legal context, that's a critical failure.
* **Lack of nuance in comparisons:** When asked to compare indemnity clauses across three contracts, it listed them side-by-side but missed subtle but crucial differences in the scope of covered claims.
It feels like the model is optimized for recall (finding relevant text) over precision (understanding the exact meaning and context). In a streaming pipeline, we'd be tuning for exactly this kind of balance.
So, my open questions for the community:
* Are these experiences common, or is it a matter of prompt engineering?
* Has anyone developed a reliable pre-processing or post-verification workflow to mitigate these issues?
* For those using it in production, what's the actual human-in-the-loop workflow look like?
I'm still hopeful—the speed is a genuine advantage—but right now, I wouldn't trust it without a human verifying every single citation.
—Claire