What we liked best about LandingAI Agentic Document Extraction was how well it handled highly variable document layouts without requiring custom templates or vendor-specific parsing rules. We tested it on invoices and contracts with multi-column tables, nested clauses, scanned PDFs, and inconsistent formatting, and the extraction quality remained consistently strong.
The Parse + Extract workflow was especially useful because it separated layout understanding from structured field extraction. That made the system much easier to integrate into our compliance pipeline and reduced the amount of preprocessing and maintenance we normally expect with OCR-based systems.
We also appreciated how quickly we were able to move from raw PDFs to usable structured JSON that could directly power downstream RAG retrieval, clause matching, and deterministic compliance checks. Review collected by and hosted on G2.com.
One area that could be improved is observability and debugging during extraction workflows. When working with complex documents, especially long contracts with nested clauses or unusual layouts, it would be helpful to have more transparent insight into why certain fields were extracted with lower confidence or missed entirely.
We also found that tuning extraction schemas for edge cases still requires some experimentation, particularly for highly domain-specific financial documents. Better tooling around validation, confidence scoring, and extraction previews would make iteration faster for developers building production-grade workflows.
That said, the overall extraction quality and flexibility were still significantly better than traditional template-based OCR systems we’ve worked with. Review collected by and hosted on G2.com.