Kita Capture
Every document. Every number. Every fraud signal — in seconds.
Pulls credit signals from any document — typed, handwritten, photo, scan — and unifies them into one structured view ready for the decision. Not raw data for your team to clean.
Processing documents across the markets we serve
Pixels in. Structure out.
Kita Capture reads any document with vision language models — typed, handwritten, photo, or scan — and returns clean, schema-checked structured data. Fraud signals run alongside extraction, not after.
Bank of the Philippine Islands
01 Oct 2025 — 31 Oct 2025
{
"applicant": {
"name": "Maria Santos Reyes",
"account_no": "8432-1184-22",
"institution": "BPI"
},
"period": { "start": "2025-10-01", "end": "2025-10-31" },
"cashflow": {
"opening": 482183.65,
"inflow_total": 1247852.10,
"outflow_total": 1809460.88,
"closing": 640552.77,
"net_recurring_credits": 142500.00
},
"transactions": 73,
"confidence": 0.98
}Upload via API, SDK, or portal. Any format. PDFs, photos, scans, screenshots.
A vision language model reads the document holistically. Layout, context, tables, handwriting.
Tampering, metadata inconsistencies, and forgery patterns calibrated to each market.
Credit signals out, not raw fields. Metrics, qualitative reads, metadata, fraud, ready for the underwriter.
Credit signals out, not raw data.
Capture reads context, structure, and content the way a trained analyst would. Then it returns the credit signals your underwriters actually use, metrics, qualitative reads, metadata, fraud.
- Credit-signal extraction — Metrics, qualitative reads, metadata, and fraud, every kind of signal an underwriter needs, pulled from one document and ready to feed your policy engine.
- Reads like an analyst — Layout, tables, handwriting, context. Catches the things a template OCR misses, including the things written between the lines.
- Fraud detection — Tampering, metadata inconsistencies, and forgery patterns calibrated to each market and document type.
- Cross-document verification — References data points across multiple documents in the same file to catch contradictions, restating income, mismatched IDs, doctored balances.
- Multi-format support — PDFs, photos, scans, screenshots, handwritten records, processed identically. No pre-processing required.
- Market-calibrated — Tuned to local document formats across Southeast Asia, Latin America, and the United States. Not generic.
Any document. Any format. No templates.
Capture handles any document format out of the box. No templates, no pre-configuration required.
Python SDK
Install, authenticate, and start processing in under 10 lines. Type hints and async support included.
REST API
Language-agnostic HTTP endpoints with webhook callbacks. Output in JSON, CSV, Excel, or custom. OpenAPI spec included.
Web portal
No-code portal to upload documents, review extractions, and export results. No engineering required.
Want to know which signals actually predict default? We'll prove it on your book.
For lenders with enough history to test against, we replay Capture across your historical applications and match every extracted signal to your repayment outcomes, at any DPD horizon you track. You get back which signals split risk cleanly across borrower deciles, and which are noise. A capability, not a prerequisite.
Replay on your historical applications
Hand us a sample of historical loans and we re-run Capture across the documents, pulling the full signal pack on every borrower file.
Match each signal to repayment outcomes
At any DPD horizon you track. Early delinquency at DPD 1 or 4, mid-book at DPD 30 or 60, charge-off at DPD 90 and beyond. Per-signal, per-window.
Ranked report, what splits risk
Each signal gets an Information Value score, monotonic decile splits, and a strength label. You see which signals belong in your scorecard and which don't move the needle.