Document Intelligence
From document pile to structured data in minutes, not days.
A document intelligence platform that reads scanned and digital documents, extracts the fields and clauses you care about, checks them, and routes exceptions to a human.
01 · Overview
People still read every page
Onboarding, lending and compliance still depend on people reading passports, trade licences, bank statements, salary certificates and contracts. The work is slow and expensive, and errors creep in. Templates break whenever a layout changes, and generic OCR doesn't understand what it's reading.
Built with
- OCR & layout
- Transformer NLP
- Human-in-the-loop
Deployment
On-premise, private cloud or in-region.
02 · How it works
Read, understand, check
- 1
Read anything
OCR and layout analysis handle scans, photos, PDFs and emails, including tables, stamps, signatures and multi-column layouts.
- 2
Understand it
Transformer models fine-tuned for your document types classify each document and extract entities, amounts, dates, parties and clauses.
- 3
Check it
Extracted data is validated against business rules and other sources: does the statement balance, does the name match the ID?
- 4
Keep a human in the loop
Low-confidence fields go to a reviewer with the source highlighted. Every correction improves the model.
- Upload / email / API
- OCR & layoutTables, stamps
- Classify & extractTransformer NLP
- Validation rulesCross-checks
- Confidence routingAuto ✓ / review ↺
03 · Capabilities
Common document types
- KYC & onboarding: IDs, passports, Emirates ID, trade licences, proof of address
- Lending: bank statements, salary certificates, financial statements
- Legal & procurement: contracts, NDAs, clause and obligation extraction
- Insurance: claims forms, medical reports, policy documents
- Compliance: regulatory filings, internal policy review
04 · Outcomes
What we measure
- Less manual effort
- Share of documents processed without human touch
- Faster turnaround
- Time from submission to decision-ready data
- Higher accuracy
- Field-level precision and recall vs. manual baseline
- Full traceability
- Every value linked to its source location
Example scenarios
Illustrative- Bank onboardingKYC pack extraction and cross-checking, cutting account-opening time.
- LenderAutomatic bank-statement analysis for affordability assessment.
- Legal teamContract review with clause extraction and risk flags.
- InsurerClaims document intake and triage.
05 · Governance
Security & governance
Controls built in
- Deploy on-premise or in-region; documents never leave your environment
- Field-level confidence scores and a full audit trail
- PII redaction options
- Role-based access to review queues
Technology
- Python
- Hugging Face Transformers
- LLMs
- Tesseract
- Layout models
- FastAPI
- PostgreSQL
- Neo4j
06 · FAQ
Common questions
Do we need templates for each document?
No. The models learn document types from examples, so new layouts don’t need new templates.
What about handwriting and poor-quality scans?
These are handled with confidence scoring. Fields the model isn't sure about go to human review instead of being guessed.
Can it plug into our core banking or CRM system?
Yes. Results are delivered as structured JSON through an API, or pushed directly into your systems.
Send us 50 sample documents.
We'll show you what can be automated, with real accuracy numbers, before you commit.
Start a project
