Turn documents into structured data you can trust.
Invoices, contracts, forms, statements, scanned PDFs — we build layout-aware extraction pipelines with validation and human review, so the data that comes out is accurate enough to act on automatically.
The data is trapped in the document.
Most back-office work is a human reading a document and typing what they see into a system. It's slow, error-prone, and it doesn't scale with volume.
Document intelligence automates that — but accuracy is everything. A pipeline that's 95% right still needs a human for the 5%, so we design the review step into the system from day one. The result: most documents flow straight through, and the rest land in a fast review queue instead of a person's inbox.
The extraction pipeline we build.
Ingest + classify
Documents arrive (email, upload, API) and are classified by type — invoice vs. contract vs. form.
Layout-aware OCR
Parsers that understand tables, columns, and scans — not naive text dumps.
Field extraction
Structured extraction of the fields you need, combining model extraction with rules.
Validation
Type checks, totals that must add up, cross-field rules, and lookups against your systems.
Human-in-the-loop
Low-confidence fields route to a review UI; everything else flows straight through.
Deliver
Validated data lands in your database, ERP, or downstream workflow via API.
What we extract from what.
| Document type | Typical output |
|---|---|
| Invoices & receipts | Vendor, line items, totals, tax, dates → AP system |
| Contracts | Parties, terms, dates, clauses, obligations → CLM |
| Forms & applications | Field values, validation flags → your DB |
| Bank/financial statements | Transactions, balances, categories → reconciliation |
| IDs & KYC docs | Identity fields + verification signals → onboarding |
| Scanned & handwritten | Best-effort OCR + confidence + review queue |
Ways to engage.
- One document type
- Accuracy measured on your samples
- Go/no-go recommendation
- Multi-type classification + extraction
- Validation + review UI
- Integration to your systems
- 30-day support
- Accuracy monitoring + tuning
- New document types
- Throughput scaling
A messy PDF in. Validated, structured data out.
Layout-aware parsing, schema-driven extraction, deterministic validation, then confidence-based routing.
schema = Invoice( vendor=str, invoice_no=str, date=date, line_items=list[LineItem], total=Money,)doc = parse(pdf) # layout-aware OCR (tables, scans)data = extract(doc, schema) # model + rulesvalidate(data, [totals_must_match, date_in_range]) # deterministicroute = "auto" if data.confidence > 0.9 else "review"Most documents flow straight through; the low-confidence ones land in a fast review queue instead of producing wrong data silently.
We measure accuracy on your real samples first.
Before committing to a build, we run a proof-of-value on your actual documents and report measured field-level accuracy.
That number drives the design of the human-review step — so you automate the volume safely and keep a person on the exceptions.
Scope a pipelineCommon questions.
How accurate is it?
Do we still need people?
Can it handle our messy scans?
Where does the data go?
Send us a stack of your documents.
Share a representative sample. We'll run a quick assessment and tell you what's automatable, at what accuracy, and what it would take.