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.

Accuracy 98%
Per document <30s
Document types & counting 50+
Used across Microfinance·SME & commercial·CDFI

Processing documents across the markets we serve

  • Bank statements
  • E-wallet records
  • Government IDs
  • Tax documents
  • Payslips
  • Audited financials
  • Business financials
  • Credit reports
  • Utility bills
  • Mobile money records
  • Insurance documents
  • Invoices & receipts
  • Loan applications
  • Property deeds
  • Vehicle registrations

How it works

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.

Extracting

bpi_statement_oct.pdf · page 3 of 14

Extracting

BPI

Bank of the Philippine Islands

Statement of Account

01 Oct 2025 — 31 Oct 2025

Account holder Maria Santos Reyes

Account no. 8432-1184-22

Currency PHP

Total credits ₱ 1,247,852.10

Total debits ₱ 1,809,460.88

Closing balance ₱ 640,552.77

Date          Description                                Amount            Balance
06 Oct       Salary credit · Reyes Trading Co.         + 142,500.00      492,103.55
07 Oct       GCash transfer · 09177234278            + 432.10          1,247,852.10
12 Oct       CASH DEPOSIT · branch-0142              + 1,809,460.88      1,809,460.88
15 Oct       SSS contribution                           4,250.00          1,803,210.88
21 Oct       Loan repayment · BPI Personal Loan      18,492.00         916,331.55

Structured output / bank_statement
Schema OK

{
  "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
}

Fraud signals

1 signal
Font mismatch on transaction line page 2 — 6th row · low

Statement totals reconcile

opening + credits − debits = closing

Document metadata intact

PDF producer matches BPI fingerprint

No duplicate of prior submitted file

perceptual hash unique vs 27 prior files

Capabilities

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.
{
  "document_type": "bank_statement",
  "institution": "BDO Unibank",
  "period": "Sep–Dec 2025",
  "avg_monthly_balance": 142580.50,
  "net_inflow_3mo": 485200.00,
  "dscr": 2.4,
  "income_pattern": "stable_recurring",
  "unusual_activity": false,
  "fraud_score": 0.02,
  "tampering_detected": false
}

Coverage

50+ document types

Capture handles any document format out of the box. No templates, no pre-configuration required.

  • Bank statements
  • E-wallet records
  • Government IDs
  • Tax documents
  • Payslips & income
  • Business financials
  • Credit reports
  • Utility bills
  • Mobile money
  • Invoices & receipts
  • Employment records
  • Insurance documents

Integration

Deploy in minutes

  • 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.

Signal validation

On request, for lenders with a book

Want to know which signals actually predict default? We'll prove it on your book.

  • 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.

Try it

Free credits in the portal

Underwrite more borrowers. Faster. With cleaner data.