Document Processing & Data Extraction

Turn documents into usable data and actions

People retype what documents already say, and every error becomes somebody else’s problem downstream.

What this means

Documents arrive as PDFs, scans, forms, and attachments, and their contents have to become ledger entries, records, and actions. A document AI system combines OCR, layout-aware extraction, and deterministic validation so every field is either proven or sent to a person. Extraction results are checked before they enter the next system. Uncertain results are routed for review.

Problems this solves

  • Invoices, statements, forms, and scans that must be keyed into systems by hand
  • Extraction tools that are wrong silently, with no confidence signal or review path
  • Document-heavy intake that slows onboarding, billing, or claims
  • Systems that receive documents but not the data inside them

Systems we build

  • Invoice, statement, and receipt extraction into accounting systems
  • Contract and agreement field extraction with source-linked review
  • Forms, applications, and onboarding packets turned into structured records
  • Scanned and handwritten material read with confidence scoring
  • Email attachment ingestion and classification
  • Document classification and routing before extraction
  • Validation rules that must tie: totals, dates, identifiers, and references
  • Downstream updates to ERP, finance, CRM, or case systems
System path
  1. Documents in
  2. Classify
  3. OCR + extraction
  4. Validation rules
  5. Review queue
  6. ERP / system of record
Anything below confidence or that fails validation goes to a person with the source attached.

Who this is for

  • Finance and accounting teams processing document volume
  • Property and multi-entity operations with statements and notices
  • Professional services firms with intake and engagement paperwork
  • Operations teams whose backlog is a pile of documents

Common integrations

  • Accounting platforms and ERPs
  • Email, shared drives, and document management systems
  • OCR, layout, and document-model providers behind an adapter layer
  • Databases and data warehouses for structured output
  • Review queues and ticketing systems for exceptions

How an engagement works

  1. 01Profile the real documents: formats, volumes, quality, and edge cases
  2. 02Build a labeled sample set and measure field-level accuracy before rollout
  3. 03Implement extraction, validation, confidence handling, and review as one pipeline
  4. 04Monitor accuracy over time and retrain or adjust rules as document mix changes

Controls and validation

  • Every field carries a confidence signal and a link to its source page
  • Deterministic validation catches anything that does not tie
  • Uncertain extractions route to a person instead of into a system of record
  • Accuracy is measured on your documents, not demonstrated on clean samples

What you receive

  • An extraction pipeline with a review surface
  • Field-level accuracy results on a sample of your real documents
  • Validation rules and rejection handling documented
  • Downstream updates that are traceable to the source document

What this does not promise: No extraction is presented as perfect. Confidence thresholds, validation, and human review are the mechanism that keeps imperfect reads out of your records.

Products with this capability

Engineering behind it

Questions buyers ask

What about handwriting and bad scans?

Those cases are exactly why confidence handling exists. The system reads what it can, attaches a confidence signal, validates what must tie, and sends the rest to a person with the source page beside it.

Do we need to replace our document storage?

No. The pipeline reads from where documents already arrive and writes structured results to where the work happens. Existing storage and systems stay in place.

How is accuracy proven before go-live?

A labeled sample of your own documents is processed and scored field by field. Go-live thresholds are agreed from that evidence, not from a vendor benchmark.

What happens to rejected documents?

They are quarantined with the reason and the source attached, and routed to an owner. Silent failures are treated as defects.

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