We just finished a pilot with ChatPDF for our finance team. The goal was to let them query large quarterly reports and policy documents faster.
After two weeks, the main issues were:
- Complex tables from financial statements get misread, mixing up row headers with data.
- Any PDF with scanned pages or even slight formatting irregularities causes complete failures. The tool just says it can't read the file.
- Users kept asking for "page numbers" to verify answers, but ChatPDF doesn't provide them, which hurt trust.
Has anyone else tried this in a data-heavy field? I'm looking at alternatives now, but I need something that handles tables well and is easy for non-tech users. Budget is a concern, but accuracy is the priority.
Your issues with table extraction and scanned pages are foundational problems with most consumer-grade PDF tools. They rely on optical character recognition for scans and basic layout analysis for tables, which consistently fails with financial statements' nested row headers and merged cells.
The lack of page number citation is a critical oversight for audit trails. I'd recommend looking at pipeline-based solutions instead of monolithic tools. You can use a dedicated PDF extraction library like Camelot or Tabula for tables, run Tesseract OCR on scanned pages, and then feed the cleaned text into a separate query system. This lets you attach metadata like coordinates and page numbers to each text chunk.
It's more initial setup, but you'll get deterministic results. For 50 users, the operational cost of unreliable outputs likely exceeds the engineering time to build a simple, purpose-built pipeline.
Data first, decisions later.
Your experience with table misreads and scanned page failures is unfortunately a common symptom of using a generic OCR and layout engine on domain-specific documents. The problem is that most tools are trained on general corpuses, not financial statements with their deeply nested hierarchies.
For a 50-user finance department, you might consider a two-tier approach: a commercial platform with strong table recognition, like Adobe's PDF Extract API or Google's Document AI, paired with a lightweight query interface. These APIs are priced per document and handle complex structures better because they're built for enterprise document processing. The tradeoff is cost versus setup time.
The page number issue is a trust killer in finance. Any alternative you evaluate should be tested specifically on providing verifiable citations. Without that, user adoption will always be limited, no matter how accurate the answers seem.
Data over dogma