Document work is the clearest case for AI automation in most businesses: high volume, entirely manual, deeply tedious, and measurably error-prone. It is also where earlier technology genuinely failed — traditional OCR needed fixed templates and broke whenever a supplier redesigned their invoice. Language models read documents by understanding them, which removes the template problem entirely.

Who this is for

Finance teams keying supplier invoices. Legal and procurement teams reviewing contracts for specific clauses. Operations teams processing forms, delivery notes or applications. Anyone whose staff retype information that arrived as a PDF.

Problems we solve

Template-dependent OCR. Extraction that breaks every time a document layout changes.
Manual re-keying. Hours of transcription with a predictable error rate.
Errors found late. A mistyped figure discovered at month-end reconciliation.
Unsearchable archives. Thousands of scanned documents with no extracted data.
Blind trust in extraction. No confidence signal, so wrong values pass through unnoticed.

What we build

Ingestion from email attachments, scanners, drives and upload forms
Classification by document type before extraction
Field extraction to a defined schema, with types and formats validated
Cross-checks against your own records — does this PO exist, does the total add up
Per-field confidence scores, with low-confidence values queued for human review
Review interfaces showing the extracted value beside the source document region
Posting into accounting, ERP or line-of-business systems
Searchable archives with extracted metadata

How we work

We treat extraction as measurable rather than assumed. A labelled sample of your real documents becomes the accuracy benchmark, and we report per-field accuracy before go-live so you can set review thresholds deliberately. Fields that fail validation or fall below confidence go to a human — the goal is eliminating routine transcription, not eliminating oversight. Arithmetic and referential checks catch a meaningful share of errors that confidence scores alone miss.

Technologies we use

Vision-capable models from OpenAI, Anthropic and Google for document understanding, with self-hosted options where documents cannot leave your network. n8n for orchestration, structured output with schema validation, and integrations into accounting and ERP systems. Extracted data indexed in PostgreSQL, with vector search where documents also need semantic retrieval.

Business benefits

Processing time per document falls from minutes to seconds
Transcription errors largely disappear from the process
Documents processed on arrival rather than in a weekly batch
Historic archives become searchable and reportable
Staff review exceptions instead of typing every field

Where it pays off

Supplier invoices matched to purchase orders and posted to the ledger
Contracts reviewed for renewal dates, liability caps and specific clauses
Receipts and expense claims validated against policy
Application and onboarding forms turned into records
Delivery notes reconciled against orders
Identity and compliance documents checked and filed

Common questions

How accurate is it?

Accuracy varies by document quality and field type — printed totals extract far more reliably than handwritten notes. We benchmark on your actual documents and report per-field figures rather than quoting a headline number that would not hold.

Does it work on scans and photos?

Generally yes, including photographs taken on a phone. Very poor scans reduce accuracy, which is exactly what confidence scoring is for.

Do we still need to check the output?

Check the exceptions, not everything. Confidence thresholds and validation rules decide what needs eyes, and you set where that line sits based on the cost of an error in your process.

Still keying documents by hand? Send us fifty real examples and we will benchmark extraction on them.

Document Processing & Data Extraction