What happens when extraction is uncertain?

Each field carries a confidence score, and anything below threshold or failing a validation rule goes to a review queue with the source document displayed alongside. Nothing uncertain posts silently.

Does it do the arithmetic?

No — deliberately. Extraction is AI; totals, balances, and reconciliation run in deterministic code with validation rules. Language models read documents here, they do not compute.

What accuracy should we expect?

Clean native PDFs of a consistent format extract very reliably. Poor scans and highly variable layouts are harder. We benchmark on your actual documents before committing to a threshold, and the confidence scoring means low-certainty extractions get reviewed rather than silently posted.

Do we still need someone reviewing documents?

For a much smaller share. The goal is that your team reviews the exceptions rather than every document — which typically means a fraction of the volume, handled in seconds each rather than minutes.

Document AI · Financial Services

Document AI for Financial Services

Statements arrive in forty formats and get keyed by hand.

Bank statements, invoices, receipts, and client documents arrive as PDFs, scans, and photographs in every conceivable layout, and somebody types them into a system. Document AI extracts the fields, validates them against your rules, and posts only what passes.

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Per document: SecondsPer document
Field confidence: ScoredField confidence
Full trail: AuditedFull trail

Typical stack

Azure Document AI
OpenAI Vision
Python
Postgres
Queue Workers

Use cases

What this handles for financial services firms

Extract transactions from statements across any bank or format
Process invoices and receipts into your accounting system with coding applied
Capture and verify KYC documents during client onboarding
Validate totals, dates, and references before anything is posted

Document backlogs cleared automatically, with staff reviewing only what failed validation.

Context

The financial services problem underneath it

Financial services work is document-heavy, deadline-driven, and unforgiving of errors — which is exactly the profile automation suits, provided everything it does is logged and reversible. We build systems where every extraction, calculation, and posting leaves an inspectable trail.

Statements, invoices, and receipts arrive in every format and get keyed by hand
Reconciliation is monotonous, high-volume, and expensive when it goes wrong
Client onboarding and KYC involve chasing documents for weeks
Reporting packs are assembled manually against a hard deadline every period

What we build

Inside a document AI pipeline

We design for the ninety percent and route the rest to people. Documents that extract cleanly and pass validation flow straight through. Anything below your confidence threshold, or failing a business rule, lands in a review queue where a person fixes it in seconds — and that correction feeds back into extraction quality.

OCR and layout parsing for scans, photos, native PDFs, and email attachments
Field extraction with per-field confidence scoring rather than all-or-nothing output
Validation rules that check totals, dates, references, and cross-document consistency
Human review queues for low-confidence extractions with single-screen correction
Straight-through posting into ERP, accounting, or CRM systems
Classification that routes each document type to the right pipeline automatically

Deliverables

What ships with the engagement

Extraction schema per document type agreed with your team
Accuracy benchmark measured on a sample of your real documents
Review queue interface for exceptions
Integration writing extracted data into your system of record
Retention and audit configuration matching your compliance requirements

FAQ