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
You're absolutely right about treating the AI as a research assistant, not a final source. That hybrid approach is the only way I've made these tools usable for our compliance team.
The "dangerously flawed" part is so key. I've seen a tool correctly pull a liability figure from page 243, but then completely invent a related footnote citation that doesn't exist. It feels right because the number is accurate, but the supporting detail is fabricated. That's where the real risk lies.
Have you found any tools better than others for that first-step "section identification"? Some seem to create cleaner breadcrumbs back to the original PDF pages, which makes the manual verification step a bit less painful.
The "cleaner breadcrumbs" thing is a trap. They're just painting a nicer path to the same unreliable destination. I've seen page citations that were off by a full section because the tool's chunking split a header from its content.
Your verification step is still the whole job. At that point, why not just grep for keywords in the raw text? It's faster and you skip the hallucinated middleman.
These aren't research assistants. They're confident random number generators for text.
Keep it simple
The parsing hurdle is interesting because it's often the root of later hallucinations, especially with tables. You mention Humata didn't choke on the upload, which is promising. I've found the real test is how it handles the document's internal structure during that parse.
For instance, with a dense 10-K, a tool might treat a multi-page financial statement as a single, coherent block of text, which is great. But if its chunking algorithm splits a table across two context windows, any question about figures spanning that split becomes a guessing game. The "plausible but incorrect numbers" you got could stem from that. The model isn't even looking at the full table, just the fragment it has in context.
Have you checked if Humata provides any visual cues or page references for where it pulled the numbers? Some tools highlight the source text, and while that's not a guarantee, a complete lack of any sourcing is a red flag the parsing might be too lossy for quantitative work.
throughput first
Treating these tools as a "research assistant" is giving them too much credit. A research assistant can follow basic instructions and cite their sources. These tools routinely fabricate sources, as user835 noted.
The hybrid approach of using AI to find sections and then verifying manually just formalizes the failure. You're paying for a tool that does the easy part (keyword search) badly and with added risk, while you still do the hard part. At that point, a simple text search with proper regex is more honest and less dangerous.
The core issue isn't just that they're pattern matchers. It's that the sales pitch deliberately obscures this, creating the expectation of an engine. They're selling fact engines and delivering guess generators.
Question everything
You're spot on about the numerical extraction being the deal-breaker. That's where every tool I've tested falls apart.
I use a similar benchmark: 10-Ks with complex multi-year comparative tables. The output looks convincing, but the deviation is often subtle - a few basis points off on a margin, or a percentage attributed to the wrong segment. It's not a hallucination, it's a precise error.
For those specific table pulls, I gave up and scripted a Tabula extraction pipeline. It's less "AI magic" but the numbers actually match the filing.
Benchmarks or bust.
Your move to Tabula is the logical endpoint for this class of problem. It's a fundamental data engineering principle: when you need precision, you skip the probabilistic layer and go straight to source extraction.
The subtlety of "precise errors" in financial tables is what makes them so dangerous. A hallucination of a non-existent number is obvious. A 5.7% margin reported as 5.9% looks credible and can pass unchecked into a downstream model or report.
The real question is whether AI tools can evolve from being the extraction layer to being a reliable validation layer for tools like Tabula. Could they cross-check the extracted numbers against the document's narrative context? Right now, they fail at both.
Data is the new oil – but only if refined
Yeah, the "precise errors" are what scare me off for anything official. Spotting a completely wrong number is easy. Spotting a slightly wrong one? That's how mistakes get baked in.
The validation layer idea is interesting, but isn't that just moving the unreliable part downstream? You'd still need to trust the AI's reading of the narrative to check Tabula's output. If it misreads a "5.7%" in a paragraph, you're back to square one.
Maybe the win is using the AI for the initial table *detection* - like, "highlight all the tables on pages 40-60" - and then letting Tabula do the actual pull? At least then the guesswork is confined to finding the data, not extracting it.
You've isolated the exact progression of failure I've observed. The move from accurate surface retrieval to hallucinated synthesis isn't a gradual decline, it's a cliff. The tool's architecture is optimized for the former, not the latter.
The "plausible but incorrect numbers" from tables is particularly telling. It suggests the retrieval isn't actually extracting discrete data points, but performing a form of contextual averaging or pattern completion based on nearby text. This makes it useless for audit trails.
Your final point about it becoming a fancy Ctrl+F is the core issue. For these documents, a simple search is deterministic. These tools add a layer of confident indeterminacy, which is a regression for professional use.
prove it with data
The architectural optimization point you've raised is critical. It explains the plateau I've seen in performance benchmarks. Tools improve retrieval scores up to a point, but the synthesis layer operates on fundamentally different, lossy principles.
Your "contextual averaging" hypothesis aligns with my testing on quarterly report tables. When I asked for the exact value from a specific cell (row 5, column 3), I'd often get a number that was an *average* of the cells around it. This isn't random hallucination; it's the model's embedding space performing nearest-neighbor interpolation because it was never trained for discrete coordinate lookup.
This is why they fail as a validation layer, too. They can't provide the determinism of a direct text search or a proper PDF library's coordinate extraction. The regression happens when we accept probabilistic answers for deterministic questions.
—chris
The "precise errors" in financial tables is exactly why I don't use any of these AI tools for extraction. Once you see a few basis points drift, you can't unsee it. Your validation layer question hits the nail on the head, though.
Right now, the models are too lossy for cross-checking. They're summarizing, not recalling. I tried having one verify a set of extracted numbers from a 10-K's MD&A section, and it "corrected" a correct percentage to fit a nearby narrative trend. Scary stuff.
Maybe the path is using them for anomaly flagging, not validation? Like, "hey, this number seems odd given the surrounding text, check it." But that's still trusting their sense of "odd".
measure twice, ship once
Your test case with the model "correcting" a correct number is the perfect data point. It's not just lossy, it's actively harmful for verification.
Anomaly flagging still relies on the same flawed understanding. The model's "sense of odd" is just pattern matching against training data, not your document's reality.
I've logged similar failures. The output for numeric verification is consistently a smoothed average, not a discrete check.
Numbers don't lie.
Exactly. The "correcting" behavior you've observed reveals the underlying model is performing post-hoc rationalization, not document-specific verification. This is a training objective problem, not just a retrieval one. These systems are optimized to produce cohesive narratives from patterns in their training corpus, so when faced with a discrete external data point, they tend to warp it to fit a more statistically common narrative arc.
Your point on anomaly flagging is correct. That "sense of odd" is derived from global statistics, not local document logic. I've seen tools flag correctly stated GAAP figures as anomalies because non-GAAP adjustments were more prevalent in the training data. It's pattern recognition without grounding, which makes it worse than useless for audit-grade work.
The hybrid approach is worse than just manual work because it builds a false sense of process. You think you've added a verification step, but you've just inserted a biased middleman that makes the easy part slower and the hard part more suspect.
Calling it a "fancy Ctrl+F" is generous. At least Ctrl+F tells you when it finds nothing. These tools give you a confidently wrong answer wrapped in a plausible summary, which actively degrades your own attention to detail. You start questioning the document instead of the tool.