Building an Evaluation Framework for Document AI Systems

Standard NLP benchmarks don't capture what matters in production document AI: can a real user trust the output enough to make a business decision?

Beyond Accuracy Metrics

Traditional metrics (precision, recall, F1) are necessary but insufficient. We need to measure:

Field-level accuracy: Per-field error rates, not just document-level scores
Confidence calibration: Do predicted confidence scores match actual accuracy?
Error modes: What types of mistakes occur, and how costly are they?
User trust: Would a domain expert catch the error before acting on it?

Our Framework

We built a multi-dimensional evaluation system:

1. Synthetic Test Sets

Generate documents with known ground truth, covering edge cases:

Missing fields
Conflicting information
Unusual formatting
Handwritten annotations

2. Real-World Validation

Partner with beta users to validate extractions against their manual reviews, tracking:

Time saved
Errors caught vs. missed
Confidence threshold tuning

3. Continuous Monitoring

Log every extraction in production with:

Model version
Confidence scores
User corrections
Time to review

Insights

After 6 months of testing, we learned:

Confidence scores must be calibrated per field type (dates vs. dollar amounts behave differently)
Users trust the system more when it flags uncertainty
95% accuracy isn't enough—the 5% needs to be the right 5% to flag

Open Questions

How do we measure "usefulness" directly? Time saved is clear, but what about decision quality improvements? We're exploring ways to quantify downstream impact.

Published December 2024