AI Document Intelligence
AI document intelligence for lending.
Read every document. Pull credit signals, not raw fields. Flag fraud. Hand the next layer of your stack a decision-ready file.
Kita is the AI document intelligence layer for modern lenders. We process any document the borrower sends. Output is structured, cited, and ready for the credit model.
Definition
What is AI document intelligence?
AI document intelligence is the use of vision and language models to read documents the way an analyst would: layout, tables, handwriting, context, signatures, and stamps. Unlike OCR, it does not require templates. The output is structured data plus metadata, fraud signals, and references back to the source document.
How it works
01
Any document, any format
PDFs, photos, scans, screenshots, handwritten records. 50+ document types and counting. No pre-processing. No template configuration per form.
02
Credit signals, not raw fields
A vision-language model reads the document holistically. Outputs include numbers, qualitative reads, metadata, and fraud signals, all calibrated to lender needs.
03
Fraud at the document layer
Tampering, metadata inconsistencies, forgery patterns, and cross-document mismatches. Calibrated per market because fraud patterns differ by country.
Comparison
AI document intelligence vs. template OCR.
| Aspect | Template OCR | Kita AI document intelligence |
|---|---|---|
| New document types | Weeks of template setup per form | Works on day one without templates |
| Handwriting and photos | Falls over outside clean scans | Reads typed, scanned, photographed, handwritten |
| Output | Raw extracted fields | Credit signals, metadata, and fraud verdicts |
| Cross-document checks | Not supported | Built in across the borrower file |
| Market coverage | Per-country forms and per-form setup | Calibrated for PH, ID, MX, KE, ZA, U.S. |
| Maintenance | Breaks when forms change | Generalizes; no template to maintain |
Who it's for
Built for the three lender scenarios we serve.
Microfinance
Mobile-money and e-wallet records.
Read GCash, M-Pesa, MTN MoMo, and Maya exports. Pull cash flow signals across providers and merge them into one borrower view.
SME and trade finance
Audited financials and corporate documents.
Parse audited statements, SEC registrations, tax returns, and trade documents. Reconcile across files; surface restatements and capital-structure inconsistencies.
CDFI and SBA
Personal financial statements and business returns.
Read 1040s, 1099s, P&Ls, balance sheets, and personal financial statements. Built to handle the document variety of mission and small-business lending.
The product
Kita Capture
Capture is the AI document intelligence layer in the Kita stack. It is also available as a standalone API for lenders who want to parse documents and route the signals into their own systems.
See Kita CaptureFAQ
Common questions
How is AI document intelligence different from OCR?
OCR extracts text or fields. AI document intelligence reads the document. It handles layout, context, handwriting, and cross-document reconciliation. Output is structured signals rather than raw fields, and no per-form template is required.
What document types does Kita support?
Bank statements, tax filings, payslips, audited financials, government IDs, business registrations, mobile money exports, e-wallet records, utility bills, invoices, and 50+ more. Any document, any format. Capture also handles documents outside the supported list with custom extraction.
Can Capture work outside lending?
Yes, but it is purpose-built for lending. The fraud signals, cross-document checks, and output schema are calibrated for credit assessment. The same engine works for adjacent use cases, but the value compounds in lending.
How accurate is the extraction?
Typical end-to-end document accuracy is around 98 percent on supported types. Per-field confidence is exposed in the API response so downstream systems can route low-confidence fields to human review.
How do we integrate Capture?
A REST API, a Python SDK, and a portal for low-volume use. Documents in, structured JSON out. CSV and Excel exports for spreadsheet workflows. Webhooks for async processing.
How is fraud detection different from a generic AI vision model?
Capture is trained to spot fraud patterns specific to lending documents in each market. Tampered stamps, mismatched fonts, metadata inconsistencies, restated income, and cross-document contradictions. Generic models do not have the market calibration.
Is the output explainable?
Yes. Every field comes with a citation to the source document and location. Fraud verdicts come with the specific signals that triggered them. An auditor can trace any output back to its origin.
Hand your underwriter a decision-ready file.
Talk to the team about AI document intelligence for your lending stack.