Document AI that turns messy files
into validated workflows.
Classification, OCR, layout and table extraction, and validation — built with confidence thresholds and human review, not silent extraction you have to trust blindly.
From a messy file
to a validated record.
Document classification, OCR, layout extraction, table and form extraction, validation against a schema, and integration into whatever system needs the result — a lending platform, a claims system, a CRM.
Use cases we build for most often: loan applications, invoices, contracts, onboarding documents, insurance claims, compliance evidence, and scanned forms.
| Document type | Typical fields extracted | What makes it hard |
|---|---|---|
| Loan application / underwriting file | Income, liabilities, identity fields, supporting-document cross-references | Multi-page, inconsistent formats across lenders — see fintech lending |
| Invoice | Line items, totals, tax, vendor/PO match | Table layouts vary wildly between vendors |
| Insurance claim / medical intake | Patient or claimant details, codes, dates of service | Handwriting and PHI-sensitive fields — see healthcare |
| Contract | Parties, dates, clause-level obligations | Extraction is often closer to retrieval than structured parsing |
| Compliance evidence / onboarding (KYC) | Identity documents, proof-of-address, signatures | High accuracy bar — a wrong field has regulatory consequences |
Confidence scores,
not silent guesses.
Chosen per document type — a clean invoice and a handwritten form need different tools.
Structure-aware extraction, not just raw text dumped out of order.
Every extracted field carries a confidence score, set against a threshold you agree to.
Low-confidence fields go to a person instead of being accepted silently.
Extracted data checked against expected types and ranges before it moves downstream.
Every extraction and correction logged — who or what changed what, and when.
Measured per field,
not as one number.
"94% accurate" means nothing without knowing which fields, on which documents, measured how. We report:
- Field-level precision and recall — accuracy broken down per field, not averaged away
- Straight-through processing rate — the share of documents that need zero human touch
- Exception rate — how often a document needs manual handling, and why
- Human-review load — the actual time your team spends on the exceptions
What happens
when confidence is low.
Low-confidence fields don't disappear into a silent default — they follow a defined path, and every correction feeds back into measuring (not automatically retraining) the pipeline.
One lending
document workflow.
field-level extraction accuracy on a lending-document workflow — measured on that specific document set and field schema, not a universal claim. See our related work on fintech inference cost below.
reporting by default. We don't publish a single blended accuracy number because it hides exactly the fields most likely to cause a downstream problem.
Built for documents
that shouldn't leak.
Encryption in transit and at rest, tenancy and access controls scoped per client, and regional deployment where data residency matters. For PII- or PHI-bearing documents, see our HIPAA-ready AI development and GDPR AI integration pages for the compliance-specific controls.
Extraction and retrieval
solve different problems.
| You need to… | Right fit |
|---|---|
| Pull a structured value (amount, date, party) out of a document | Document AI (this page) |
| Answer a free-form question grounded in a set of documents | RAG development |
| Both — extract structured fields and support Q&A over the same documents | Document AI feeding a RAG layer, built together |
Where the data
lands.
Document stores, email intake, S3 or blob storage, CRM, ERP, and lending or claims systems — see AI integration for how we connect to systems you already run without a platform migration.
No sales pitch,
just how we work.
We report what matters for your specific downstream risk.
Low-confidence fields are routed, never silently accepted.
Every extraction and correction logged for compliance review.
You own the pipeline and configuration at completion.
Tools we've shipped
in real projects.
Before you
book a call.
What accuracy can we expect?
It depends entirely on document quality and field complexity, which is why we measure field-level precision and recall on your actual documents. In one lending-document workflow, straight-through field-level extraction reached 94% — that figure reflects that specific document set and shouldn't be assumed to transfer to yours without testing.
How is this different from RAG or document Q&A?
Document AI extracts structured data into a schema — a loan amount, a date, a line-item total. RAG retrieves relevant passages to answer a question. They're complementary and often used together.
What happens to documents with low-confidence extractions?
They route to human review instead of being accepted silently. We set the confidence threshold with you based on how costly an error is in your workflow.
Can it handle scanned or handwritten documents?
Scanned documents, yes, reliably. Handwriting varies — we test accuracy on a representative sample before committing to a scope.
How do you handle sensitive data like PII or PHI?
Encryption in transit and at rest, scoped access controls, and regional deployment where residency matters. See our HIPAA and GDPR pages.
What systems does it integrate with?
Document stores, email intake, S3/blob storage, CRM, ERP, and lending or claims systems — wherever the extracted data needs to land.
What actually happens to an exception once it's flagged?
It routes to a review queue showing the source document and the model's best guess side by side, so a reviewer confirms or corrects rather than re-keying from scratch. Each exception is also tagged by cause — poor scan quality, an unfamiliar layout, genuine ambiguity — so recurring causes show up as a pattern, not just a raw count.
Does the accuracy number change by document type?
Significantly. An invoice with a consistent layout performs very differently from a handwritten claim form or a multi-page loan file with cross-referenced supporting documents. We scope and report accuracy per document type, not as one blended figure across everything you send us.
Send us a sample
of the documents.
Book a 30-minute call and bring a handful of representative documents — we'll give you an honest read on expected accuracy before any commitment.