Accuracy and review

Document AI accuracy you can audit.

Accuracy claims only matter when they are tied to field definitions, straight-through processing, review thresholds, benchmark data, and a log that explains every exception.

What an accuracy SLA should measure

A useful document AI SLA separates character OCR accuracy from field accuracy, document-level completion, straight-through processing rate, reviewer correction rate, and downstream defect rate. One headline number hides the actual operational risk.

Field accuracy

Was the extracted value right for the business field, not just the visible text?

STP rate

What share of documents reached the next system without human review?

Review precision

Did the workflow route the uncertain fields without flooding reviewers?

SLA metrics buyers should ask for

MetricDefinitionWhy it matters
Field accuracyCorrect extracted value by field, weighted by business impact.Separates invoice total errors from low-risk address-format drift.
Straight-through processingDocuments accepted automatically after validation and confidence checks.Measures automation yield, not just model performance.
False approval rateIncorrect fields that passed without review.The risk metric finance, lending, insurance, and compliance teams care about.
Review loadFields or documents sent to review per 1,000 pages.Prevents a high-accuracy system from quietly becoming a manual queue.
Audit completenessModel version, prompt version, source citation, validation status, and reviewer change logs.Makes the SLA defensible after an exception or audit request.

How Cogneris designs a benchmark

Start with production-like documents, not a demo set. Include clean PDFs, scanned PDFs, mobile photos, multi-page packets, tables, handwriting, missing fields, conflicting documents, and documents that should fail validation. Score field accuracy, latency, cost per resolved document, review effort, and audit evidence.

Where confidence thresholds fit

Confidence is a routing signal, not a guarantee. Cogneris combines model confidence, source citations, validation results, document quality, and tenant-specific policy rules. The workflow can auto-approve high-confidence fields, route only disputed fields, or hold an entire document when a critical field fails.

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