Full-text OCR extraction from scanned PDFs and images.
Every scanned PDF, fax, photo, or image file that enters the repository is processed by the built-in OCR engine. The extracted text is indexed for search, available for AI queries, and stored alongside the original file. Scanned contracts become searchable. Paper compliance certificates become retrievable. Documents that were previously dead ends become first-class records.
Handwritten form digitisation using ICR — no manual data entry.
Inspection forms, intake sheets, field reports, and any other handwritten content processed through Intelligent Character Recognition. The platform identifies handwritten fields, extracts the values, and maps them to your document type's metadata schema. Reject rates and low-confidence fields are flagged for human review. What used to require a data-entry team now runs automatically on ingest.
AI auto-classification that learns from your team's patterns.
The platform watches how your team tags and classifies documents, then begins doing it automatically. Contracts get extracted effective dates and counterparty names. Invoices get vendor, amount, and purchase-order number. Medical records get episode type and provider. Every classification is reviewable and correctable — corrections feed back into the model to improve accuracy over time.
Schema-validated extraction with rules, lookups, and required-field enforcement.
Extracted metadata is validated against your document type's schema before it is accepted. Required fields must be present. Date fields must parse correctly. Amount fields must be numeric. Reference numbers can be validated against external lookups. Records that fail validation enter a review queue rather than the live repository — so you never ingest dirty data silently.
What your team actually gets.
Zero manual data entry
Printed and handwritten documents are digitised and structured on ingest — no typing, no re-keying, no data-entry backlog.
Validated metadata, always
Schema validation catches missing, malformed, or out-of-range fields before they reach the live repository.
AI that learns your patterns
Auto-classification improves over time as reviewers correct suggestions — the model adapts to your document types and naming conventions.
Audit-ready from day one
Every extraction event is recorded in the tamper-evident audit ledger — who ingested, what was extracted, and when it was reviewed.
How real teams use this every day.
Insurer digitises 500,000 paper claims forms with no data-entry team
ICR extraction processes handwritten claim forms at ingest. Required fields are validated automatically. Low-confidence fields enter a review queue staffed by a fraction of the original team.
Hospital network extracts structured data from scanned patient intake forms at admission
Scanned intake forms are OCR-processed on arrival. Patient name, DOB, insurance details, and consent signatures are extracted, validated against the EMR, and stored as governed records in under 30 seconds per form.
Energy company auto-classifies 20 years of inspection reports by asset and defect type
AI classification tags every newly ingested inspection report with asset ID, defect class, and regulatory regime. Historical records are batch-processed to the same schema — one unified view of the inspection history.
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