The problem
Why document & data extraction is hard to get right
The work that ages your team is reading: statements, contracts, invoices, forms, and email threads keyed by hand into a system of record. Off-the-shelf OCR gets the easy 80 percent and silently mangles the rest, and a wrong field downstream is worse than a slow one. The challenge is extraction that is accurate enough to act on, with a clean audit trail and review only where the stakes warrant it.
How we build it
01
Layout-aware extraction
Models that read tables, multi-column forms, and handwriting across document types, normalizing to the schema your systems expect.
02
Validation and reconciliation
Cross-checks against source totals, master data, and business rules so bad extractions are caught before they propagate.
03
Confidence-routed human review
Low-confidence fields route to a reviewer; high-confidence ones pass straight through — review effort follows risk, not volume.
04
Provenance on every field
Each extracted value links back to its location in the source document, so audits and disputes resolve in seconds.
The outcome
Documents that used to sit in a keying queue become structured records within minutes — with field-level accuracy you can prove and a reviewer touching only the genuinely ambiguous cases.
Related
Key concepts
More use cases