Documents arrive in whatever shape the sender chose. An invoice from one supplier puts the total bottom-right; the next puts it in a table with three tax lines; a third arrives as a photograph taken on a phone. Traditional template-based extraction breaks the moment a layout changes, which is why so much of this work is still done by hand.
Document Intelligence combines OCR with AI extraction that reads a document by meaning rather than by coordinates. It identifies the fields you asked for wherever they appear, applies your validation rules, and hands back structured data ready to be posted into an accounting, ERP or business system.
The part that matters operationally is what happens when it is unsure. Rather than writing a doubtful value into your system, low-confidence fields are flagged for review. A human confirms the handful of uncertain cases instead of re-keying every document — and those corrections feed back into subsequent extractions.
The problem
Manual document entry is slow, repetitive and error-prone at any real volume. A person opens a PDF, reads a number, types it into another system, and repeats — hundreds of times a week. It is expensive work, it scales linearly with volume, and the errors it produces are discovered late, usually during a reconciliation or an audit.
The solution
Document Intelligence combines OCR and AI extraction to pull structured fields from documents, validate them, and export or sync to your systems. It handles layouts it has never seen, applies your business rules to catch what does not add up, and routes only the uncertain cases to a human for confirmation.
How it works
- 1
Define the fields you need extracted — invoice number, supplier, dates, line items, totals, tax breakdown, contract clauses, or any field specific to your process.
- 2
Send documents in, whether individually, in bulk, by watched folder, or through the API. PDFs, scans and photographs are all accepted.
- 3
OCR converts the document to text while preserving layout, so table structures and multi-column pages are read in the right order.
- 4
The AI locates the requested fields by meaning rather than position, which is what allows an unfamiliar supplier layout to be processed without a new template.
- 5
Validation rules run automatically: line items are checked against the stated total, dates for coherence, references against your existing records.
- 6
Fields the model is unsure about are flagged for human review. Confirmed corrections improve accuracy on the documents that follow.
- 7
Validated data is exported as structured output or synced directly into your accounting, ERP or business system.
Features
- OCR with layout preservation
- Template-free field extraction
- Line-item and table extraction
- Automatic validation rules
- Confidence scoring per field
- Human-in-the-loop review queue
- Bulk and API-driven processing
- Structured export and direct sync
Benefits
- — Processing time that no longer scales with headcount
- — Errors caught at entry rather than at reconciliation
- — New supplier layouts handled without configuration
- — Staff moved from re-keying to handling exceptions
- — A consistent, auditable trail from document to record
- — Backlogs cleared without temporary hiring
Where it fits
Typical situations this application is built for.
Supplier invoice processing
Invoices arriving from several hundred suppliers, each with its own layout, are read into the accounting system. Line items are checked against the stated total, and only the mismatches reach a person.
Contract review and indexing
A legal team extracts renewal dates, notice periods and liability caps from a back catalogue of contracts, turning a shelf of PDFs into a searchable table of the dates that actually require action.
Field forms captured on paper
Inspection sheets filled in by hand on site are photographed and submitted. Handwritten values are extracted where legible and flagged for confirmation where they are not, rather than being silently guessed.
Onboarding document checks
Identity and registration documents submitted during onboarding are read, cross-checked against the details provided in the form, and any discrepancy is surfaced before the account is approved.
Integrations
Security
- — Documents processed and retained according to your configured policy
- — Encryption in transit and at rest
- — Role-based access to the review queue
- — Full processing audit trail per document
- — Configurable retention and deletion
- — Access scoped per document type
Pricing
Pay as you go
Contact us
- — Per-page pricing
- — All document types
- — Validation rules
- — Review queue
- — Email support
Volume
Contact us
- — Committed monthly volume
- — Direct system sync
- — Custom field schemas
- — Priority processing
- — Priority support
FAQ
Does it work on layouts it has never seen?
Yes. Fields are identified by meaning rather than by fixed coordinates, so a new supplier invoice is processed without creating a template for it first.
What happens when the extraction is uncertain?
The field is flagged rather than written into your system. Each extracted value carries a confidence score, and anything below your configured threshold is routed to the review queue for a person to confirm.
Can it read handwriting?
Legible handwriting is generally read, but it is markedly less reliable than printed text. For handwritten forms we recommend a lower confidence threshold so that more values are confirmed by a person.
How are documents retained?
Retention is configurable, including deletion immediately after successful extraction. The right policy depends on your own regulatory obligations, which is something to settle during setup.
Can it extract line items, not just header fields?
Yes. Tables are extracted row by row, and line items can be validated against the document total to catch a mis-read figure before it reaches your accounts.
What formats are accepted?
PDFs, whether native or scanned, and common image formats including photographs taken on a phone. Quality affects accuracy: a flat, well-lit scan extracts more reliably than an angled photograph.
Does it get better over time?
Corrections made in the review queue inform subsequent extractions, so recurring document types tend to need fewer confirmations as they are processed.
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