Intelligent document processing
We build document processing that reads invoices, forms, contracts and referrals and turns them into structured data your systems can use. Every field is validated against your rules, and anything uncertain lands in a review queue before it reaches your records.
What it is and when it fits.
Intelligent document processing combines vision-capable language models, OCR and parsing tools to pull fields, tables and line items out of documents in whatever layout they arrive. We define a schema for each document type, validate the output against your business rules and reference data, and write the result into your ERP, CRM or case system. Low-confidence fields go to a person with the source page beside them.
It is a strong fit when your team keys in hundreds of documents a week, the layouts vary by sender, and errors cost real money or time downstream. Template-based OCR still works well for a handful of fixed forms that never change, and we will say so if that covers your case. Very low volumes rarely justify the build, so we look at volume and error cost together during discovery.
What we build.
Invoice and receipt capture
Header fields, VAT lines and line items extracted from PDFs, scans and email attachments, matched to purchase orders and suppliers before booking.
Contract data extraction
Parties, dates, renewal terms and key clauses pulled into a register, with the source passage linked so reviewers can check each value.
Forms and intake documents
Application forms, referrals and claims read into structured records, including handwriting and multi-page attachments.
Validation and business rules
Checks against master data, totals and required fields, so a wrong IBAN or a missing signature is caught at intake.
Human review queue
A focused interface where staff confirm or correct flagged fields, and every correction is stored to measure and improve accuracy.
Accuracy reporting
Field-level accuracy, straight-through rates and processing times per document type, tracked over time as layouts and suppliers change.
How it works.
- 01
Collect real documents
We gather a representative set of your documents, including the awkward ones, and agree the fields, rules and target systems.
- 02
Build the extraction pipeline
We set up parsing, schemas and validation, then measure accuracy per field against a labelled test set before anything goes live.
- 03
Go live with review
The pipeline runs on live intake with people confirming flagged fields, and we tune thresholds as confidence builds.
- 04
Extend and maintain
New document types and suppliers are added as test cases first, so accuracy on existing ones is protected with every change.
Related work.
- Finance operations
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- Healthcare
Document parsing for patient referrals, with 80% less manual intake work
80%less manual intake work- Document parsing
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- Financial services
Open-source document parsing API and MCP server for financial documents
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Built with.
All technologiesFurther reading.
Common questions.
It depends on document quality, layout variety and the fields you need. We measure accuracy per field on your own documents before launch and set confidence thresholds so uncertain values always go to a person. You see the numbers before you decide to switch over.
The number of document types, how much the layouts vary, the validation rules involved and the systems the data has to reach. Monthly running costs depend on volume and model choice. We scope both during discovery so you can compare them with the manual effort today.
We can run processing in EU regions, use EU-hosted or self-hosted models for sensitive material, and keep documents in your own cloud account. Access is logged, retention is configurable, and we agree data handling in writing before we start.
Yes, within limits. Clean scans and phone photos work well with current vision models, and legible handwriting is usually readable. Very poor images are flagged for review, and we test your worst examples early so there are no surprises later.