AI ERP with agents inside the systems you run
AI phone and voice agents for real calls
LLM.coPrivate, self-hosted LLM deployments
Legal AI infrastructure for firms
AI RFP discovery and response drafting
Automatic.coBusiness process automation
CyberAttack.aiThreat detection and defense
Secure AI virtual data roomsTurn 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
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.