IDP vendor evaluation checklist

This checklist is for anyone running a formal RFP or vendor bake-off for intelligent document processing (IDP): procurement leads, engineering managers, and ops owners who need to compare vendors on more than a demo. Use it before you send the RFP, not after you've already picked a favorite based on a slick sales call.

Scoping and requirements

• List every document type you need processed, with volume per type (vendors price and tune very differently for invoices vs. contracts vs. identity documents)
• Define your required fields per document type and which ones are mandatory vs. optional
• Decide your acceptable straight-through processing rate before human review
• Identify whether you need structured extraction, classification, or full document understanding
• Check if your use case matches an existing solution page, such as insurance claims or KYC onboarding, to borrow proven requirements

Accuracy and testing

• Request field-level accuracy numbers, not just "document accuracy" (aggregate numbers hide weak fields)
• Ask vendors to run a test on your own sample documents, not their curated demo set
• Define how accuracy is measured: exact match, normalized match, or human-graded
• Ask how the vendor handles low-confidence extractions and what the fallback path is
• Review their methodology against independent guidance like Invoice AI Accuracy: How to Test & Measure Real Performance
• Compare OCR-based and VLM-based approaches for your document types (OCR vs VLM)

Provenance and auditability

• Ask whether every extracted field links back to its exact location on the source page (field-level provenance)
• Confirm whether confidence scores are available per field, not just per document
• Ask how corrections are logged and whether that log is exportable for audit
• Check if the system retains document versions and extraction history
• If you operate under e-invoicing mandates, review AI in French E-Invoicing: Compliance & Audit Trail Challenges

Security and compliance

• Ask where documents and extracted data are stored and for how long
• Confirm SOC 2 or equivalent certification status (ask for the report, not just a badge)
• Check GDPR posture if you process EU personal data, including subprocessors
• Ask whether LLM prompt logs contain personal data and how long they're retained
• Review their published security practices directly: Security and compliance
• For LLM-based pipelines, check GDPR & SOC 2 Compliance for LLM Logging: A Guide

Integration and workflow fit

• Confirm available APIs, webhooks, and whether there's a no-code connector for your stack
• Ask how exceptions route to human reviewers and whether that queue is configurable
• Check whether the vendor supports your specific document variants (multi-language, scanned vs. digital, non-standard layouts)
• Ask about uptime history and incident response process
• If you're still deciding whether to build this in-house, read Build vs buy IDP first

Pricing and total cost

• Ask for pricing per document, per field, or per API call, and model your actual volume against it
• Check for minimum commitments, overage fees, and contract length
• Factor in the cost of human review time for low-confidence cases, not just the vendor's per-document fee
• Compare total cost against accuracy using a framework like Document Parsing: Accuracy vs. Cost Per Correct Answer
• Run your numbers through a ROI calculator before signing
• Review current rates at Pricing

Common mistakes

• Scoring vendors on a generic demo instead of your own documents, which hides edge cases that matter most to your volume.
• Treating document-level accuracy as the whole story when the field you care about most might be the weakest one.
• Skipping the provenance question because "accuracy looks fine," then struggling later to explain a bad extraction to an auditor.
• Signing a contract before testing exception handling, since the human review queue is where most operational cost hides.
• Assuming SOC 2 means GDPR-ready, or the other way round. They cover different things.
• Comparing sticker price per document without modeling your own volume and failure rate.

FAQ

How many vendors should we test in an RFP?

Three is usually enough to see real differences without drowning in comparison work. Testing more than four rarely changes the decision and burns a lot of internal time running the same sample set through each pipeline. If your document types vary widely (say, invoices and contracts together), test each type against at least two vendors to be sure accuracy doesn't swing wildly by document class.

Should we weight price or accuracy more heavily?

It depends on your review capacity. If low-confidence extractions get expensive human review, a cheaper vendor with weaker accuracy can cost more overall once you add labor. Model both scenarios with your own volume before deciding. A useful comparison point: Sygnet vs Rossum and similar pages often break down this trade-off by vendor.

Is it worth testing against named competitors directly?

Yes, if a shortlist vendor has a published comparison page against a competitor you're also evaluating, read it critically for specific claims you can verify yourself (like Sygnet vs Azure AI Document Intelligence). Treat marketing comparisons as a starting hypothesis to test on your own documents, not a final answer.