Alright, let’s get this out there. I’ve hit my quarterly “try a new AI tool” quota and this time it’s Humata’s turn in the ring. My specific torture test: a 500+ page SEC 10-K filing and an equally monstrous EU regulatory PDF. The promise is always the same: “Ask anything, get instant answers!” The reality, as usual, is messier.
I’ve run this same test on ChatGPT’s file upload, Claude, and a few other PDF-specific platforms. They all buckle in similar but distinct ways. Humata’s initial parsing seemed decent—it didn’t choke on the upload, which is a low bar but one many fail. The problems started when the questions got specific.
* **Surface-level queries:** “What’s the company’s revenue?” Fine. Accurate.
* **Cross-document synthesis:** “Compare the risk factors in section 1.2 of doc A with the mitigation strategies mentioned in doc B’s appendix.” Starts to hallucinate, blending sections that don’t exist.
* **Numerical extraction across tables:** Asked for a specific set of figures from a financial table deep in the document. It returned plausible, but incorrect, numbers. This is the deal-breaker. If I can’t trust the data pull, the tool is just a fancy Ctrl+F.
The “chat” approach feels like it’s straining against the complexity. It’s okay for navigating to a general section, but for precise, audit-ready work? I’m not convinced.
My current verdict: better than most for getting a quick gist of a massive document, but dangerously unreliable for any detailed analysis or data extraction. It’s another tool that’s 80% there, but that last 20% is the part I actually need. Anyone else thrown a real-world, dense regulatory doc at it and gotten different results? Or is this just the current ceiling for these consumer-facing AI doc tools?
You've hit on the core problem: these tools are built for the general case, not for precision work with legal and financial data. The hallucination on cross-document synthesis isn't a bug, it's baked into the model's nature. They're pattern matchers, not fact engines.
For regulatory filings, you can't afford plausible numbers. The only reliable method I've found is to combine them. Use the AI to quickly identify potential sections or page ranges, then verify *everything* manually in the source PDF. Treat its output as a very fast, but dangerously flawed, research assistant.
I gave up on getting correct table extraction from any of them. If the data's in a complex table or a scanned annex, you're better off with a proper data extraction service, or old-fashioned elbow grease. The AI will confidently lie to you every time.
Trust but verify – and audit