Documents in, structured data out
Invoices, contracts, applications, forms, processed automatically. Extract fields, classify documents, flag exceptions, and route results to the right workflow.
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Document types we process
Common document workflows where AI extraction, validation, and review queues can remove a large part of manual effort.
Invoices
Vendor name, amount, line items, VAT, due date extracted and pushed to your ERP or accounting system. Edge cases flagged for human review rather than silently misread. Accuracy benchmarked on your invoice samples before go-live.
- Vendor, amount, line items, VAT, due date extracted - field coverage defined by your ERP or accounting system requirements
- High accuracy on consistent document types - benchmarked on your actual invoice sample before go-live
- Exceptions flagged for human review - system does not guess when confidence is low, it queues for someone to check
- Pushed directly to ERP or accounting system - SAP, Oracle, QuickBooks, Xero, or custom via API
Contracts
Parties, dates, key obligations, penalty clauses extracted automatically. Non-standard terms flagged before the document reaches legal. Summary generated in plain language. Saves hours of manual review per contract.
- Parties, dates, key obligations, penalty clauses - extracted and structured into a consistent format every time
- Non-standard terms highlighted before legal review - unusual clauses surface before the document reaches a human reviewer
- Plain-language summary generated automatically - executive summary alongside the structured data for quick decision-making
- Supports your legal review workflow - integrates with DocuSign, SharePoint, or your document management system
Application forms
Intake forms, loan applications, onboarding packets. Fields extracted, validated against your business rules, and routed to the right team or system. Works for structured forms and variable-layout documents alike.
- Intake forms, loan applications, onboarding packets - structured layouts and variable-format documents both handled
- Field validation against your business rules - missing fields, out-of-range values, and inconsistencies flagged
- Routing to correct team or system - application type determines destination automatically
- Handles handwritten fields where quality permits - falls back to review queue for illegible content rather than guessing
Email-based document intake
Emails classified by intent and type, key data pulled out, tickets or CRM records created automatically. Works with your existing Gmail or Outlook inbox without changing how people send things.
- Classified by intent, type, and priority - support request, invoice, complaint, and enquiry handled differently
- Tickets or CRM records created automatically - Zendesk, Freshdesk, HubSpot, Salesforce, or custom system
- Works with your existing Gmail or Outlook inbox - no change to how senders send, no inbox restructuring
- Human review queue for low-confidence classification - uncertain cases surface for review instead of being misrouted
Compliance documents
KYC documents, certificates, licenses, regulatory filings. Fields verified, expiry dates tracked, results stored in structured format ready for audit. Reduces manual review effort by routing clean cases automatically and flagging uncertain ones for review.
- KYC documents, certificates, licenses, regulatory filings - fields verified, document type classified, validity checked
- Expiry dates tracked with automated alerts - notification sent before a certificate lapses, not after
- Structured output ready for audit - every extraction logged with source document reference and timestamp
- GDPR-aware pipeline options - on-premise or private cloud deployment for sensitive compliance data
Reports and statements
Tables extracted from PDFs and scanned documents, data consolidated across multiple reports, output to CSV or pushed directly to your data warehouse or BI tool. Handles irregular table formats and multi-page documents.
- Tables extracted from PDFs and scanned documents - multi-page tables, merged cells, and irregular layouts handled
- Data consolidated across multiple reports - same metric across 20 weekly reports merged into one clean dataset
- Output to CSV or data warehouse - Snowflake, BigQuery, Redshift, or direct database insert
- Handles irregular table formats - configured around your specific report format, not a generic template
How document processing is built
- 01
Document audit
We collect representative samples across all document types you handle. We map fields, layout variants, quality issues, languages, and edge cases before choosing the pipeline.
- 02
Pipeline design
OCR layer, extraction model, validation logic, exception routing. We pick the right tool for each step rather than applying one approach to everything.
- 03
Build and calibrate
Pipeline built against your real documents. Accuracy benchmarked before anything goes near production.
- 04
Connect and deploy
Hooks into your inbox, file storage, ERP, or custom API. Human review queue catches anything below the confidence threshold.
- 05
Monitor and improve
Extraction accuracy, failed documents, and low-confidence fields tracked over time. When new document variants appear, the pipeline is adjusted before errors pile up.
Common questions
What accuracy should we expect?
For clean PDFs with consistent structure, 95–99% field accuracy is often realistic. For poor scans or highly variable layouts, 85–92% may be more realistic, with a review queue handling the rest. We benchmark before go-live so you know the numbers upfront.
Can it handle scanned documents and photos?
Yes. We layer OCR, Tesseract, AWS Textract, or Google Document AI depending on volume and quality, before the extraction step.
Does the data leave our infrastructure?
It depends on the pipeline design. For sensitive documents, we can build fully on-premise or private-cloud setups where nothing touches third-party APIs.
What format does the output come in?
The output format is defined by your workflow: JSON for API consumption, CSV for spreadsheets, direct database inserts, or webhook calls to your existing system.
Can it handle multiple languages?
Yes, depending on document quality, language pair, and field complexity. This is especially relevant for EU companies receiving documents from across Europe. We test language detection and multilingual extraction during the proof of concept.
How much does AI document processing cost?
A short scope check can start from €1,000–5,000. A focused proof of concept usually ranges from €3,000–15,000. A first production pipeline with OCR, extraction, validation, review queues, and system integration usually starts from €7,000–30,000. More complex document sets are estimated after we review your samples, formats, field list, volume, and security needs.
What happens when the system is not confident?
Low-confidence fields are routed to a human review queue instead of being accepted automatically. The system records the source document, extracted value, confidence score, and reason for review so your team can correct it quickly.
Do documents need to follow one fixed template?
No. We can handle multiple layouts, but the expected accuracy depends on how consistent the documents are. Stable formats are easier to automate. Highly variable scans, handwritten fields, or poor image quality require more validation and review logic.
Still processing documents by hand?
Send us a few sample documents and the list of fields you need. We will tell you what can be automated, what needs review, and what the first version should include.
Show us your documents →