AI Document Agents
Extract the fields you define from PDFs, emails, and forms, validate them against your own rules, and send low-confidence documents to a reviewer instead of downstream systems.
The Problem
- • Manual data entry scales only by adding headcount
- • Layouts vary by vendor, region, and document version
- • Scanned and photographed documents reduce accuracy
- • Transcription errors surface late, in finance or audit
- • There is no record of why a value was entered
The Solution
- ✓ Extract a defined schema of fields per document type
- ✓ Validate against formats, totals, and reference data
- ✓ Attach the source page and region for every field
- ✓ Hold low-confidence extractions for human review
- ✓ Record each decision for audit and quality review
Pilot Metrics
Capture a baseline first, then compare the pilot against the same document mix, field set, and accuracy standard.
Quality
Field-level accuracy and validation pass rate
Speed
Time from document received to verified record
Operations
Review rate, rework, and cost per document
Suggested Pilot Scope
- • One document type and one owning team
- • A defined field schema with required and optional fields
- • Review-before-commit mode before any system write
- • Named reviewers and an agreed confidence threshold
Inputs We Need
- • Sample documents with sensitive information removed
- • The field schema and current validation rules
- • Document types that always require human sign-off
- • Baseline accuracy, turnaround, and volume measures