I'm currently evaluating AI-powered document tools specifically for extracting structured data from financial reports (10-Ks, annual reports) for integration into our ERP system. The primary use case is pulling tables—think income statements, balance sheets, and cash flow statements—into a consistent format for analysis.
My initial tests with both ChatPDF and Humata have yielded mixed results. Both can answer questions about the text, but reliable table extraction is my core requirement.
I've created a small comparison matrix based on my preliminary findings:
* **Table Recognition Accuracy:**
* ChatPDF: Often identifies a table's presence but returns the data as a formatted text block, not a structured array. Merged cells or multi-line headers frequently cause misalignment.
* Humata: Appears slightly better at preserving the row/column relationship in simpler tables. However, it can still struggle with tables spanning multiple PDF pages.
* **Output Format:**
* Both tools primarily output table data within their chat interface. Neither offers a native "export to CSV" or similar function for identified tables, which adds a manual step.
* **Handling Complex Financial Formats:**
* Footnotes and asterisks in tables often get intermingled with the numerical data.
* Segmented or departmental breakdowns within a single report are not consistently isolated by either tool.
Has anyone conducted a more rigorous, side-by-side analysis for this specific table extraction task? I am particularly interested in:
* Which tool provides more consistent, machine-readable output for tabular data?
* Are there specific prompting techniques that improve table extraction reliability in either platform?
* Does one handle scanned PDFs (image-based tables) better than the other, perhaps via integrated OCR?
I plan to run a controlled test with a set of 10 recent annual reports and will share my spreadsheet results. Any prior experience or data points would help refine my testing parameters.
Measure twice, buy once.
Totally feel your pain on the output format issue. That "formatted text block" from ChatPDF is useless for any automated pipeline. I've had to write annoying regex cleanup scripts just to get it into a dataframe, which defeats the purpose.
You mentioned Humata struggling with multi-page tables - that's a killer for financial reports. I've found that segmenting the PDF into single pages first can sometimes improve accuracy, but then you lose the table context across the segmentation. It's a messy workaround.
Honestly, for your ERP integration use case, you might be better off skipping these generalist tools. Have you looked at a dedicated library like Camelot or Tabula, paired with some GPT-4-vision prompts for the weird ones? It's more engineering, but the structure is deterministic.
"Skip these generalist tools" is the only sensible take here. The moment you have to write cleanup scripts for their output, you've become their unpaid product dev.
But swapping one DIY project for another by suggesting Camelot + GPT-4-vision is still a trap, just a different vendor. You're now on the hook for managing API costs, prompt engineering, and stitching it all together. That's not a solution, it's a part-time job.
Better to ask what the ERP vendor itself supports or what extraction service they've white-labeled. At least then the lock-in is someone else's problem.
Trust but verify.
You're missing a key column: cost per doc at scale. Their pricing isn't built for processing hundreds of reports.
Your note on Humata struggling with multi-page tables is the deal-breaker. Most financial statements span pages. If it can't handle that, the slightly better structure is irrelevant.
Have you checked if either tool has an actual API? A chat interface for ERP integration is a non-starter.