Capture50+ doc types, any format.Docs

Kita Capture

Every document. Every number. Every fraud signal.
In seconds.

Capture leverages vision language models to pull the richest credit data from any document, regardless of format or submission type. Optimized for the most complex, local documents across every market we serve, built for the realities of lending on the ground.

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Accuracy0%
Per document<0s
Document types & counting0+

Processing documents across the markets we serve

Bank statementsE-wallet recordsGovernment IDsTax documentsPayslipsAudited financialsBusiness financialsCredit reportsUtility billsMobile money recordsInsurance documentsInvoices & receiptsLoan applicationsProperty deedsVehicle registrationsBank statementsE-wallet recordsGovernment IDsTax documentsPayslipsAudited financialsBusiness financialsCredit reportsUtility billsMobile money recordsInsurance documentsInvoices & receiptsLoan applicationsProperty deedsVehicle registrationsBank statementsE-wallet recordsGovernment IDsTax documentsPayslipsAudited financialsBusiness financialsCredit reportsUtility billsMobile money recordsInsurance documentsInvoices & receiptsLoan applicationsProperty deedsVehicle registrations

Pixels in. Structure out.

Kita Capture reads any document with vision language models, whether typed, handwritten, photo, or scan, and returns clean, schema-checked structured data. Fraud signals run alongside extraction, not after.

bpi_statement_oct.pdf · page 3 of 14
Extracting
BPI

Bank of the Philippine Islands

Statement of Account

01 Oct 2025 to 31 Oct 2025

Account holderMaria Santos Reyes
Account no.8432-1184-22
CurrencyPHP
Total credits₱ 1,247,852.10
Total debits₱ 1,809,460.88
Closing balance₱ 640,552.77
DateDescriptionAmountBalance
06 OctSalary credit · Reyes Trading Co.+ 142,500.00492,103.55
07 OctGCash transfer · 091772342788,432.101,247,852.10
12 OctCASH DEPOSIT · branch-0142+ 1,809,460.881,809,460.88
15 OctSSS contribution4,250.001,803,210.88
21 OctLoan repayment · BPI Personal Loan18,492.00916,331.55
Structured output / bank_statementSchema 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 signals1 signal
Font mismatch on transaction linepage 2 · 6th row · low
Statement totals reconcileopening + credits − debits = closing
Document metadata intactPDF producer matches BPI fingerprint
No duplicate of prior submitted fileperceptual hash unique vs 27 prior files
01 · Send

Upload via API, SDK, or portal. Any format. PDFs, photos, scans, screenshots.

02 · Read

A vision language model reads the document holistically. Layout, context, tables, handwriting.

03 · Verify

Tampering, metadata inconsistencies, and forgery patterns calibrated to each market.

04 · Extract

Credit signals out, not raw fields. Metrics, qualitative reads, metadata, fraud, ready for the underwriter.

Score thin-file borrowers in real time.

In thin-file markets, the credit bureau returns little or nothing on first-time and informal borrowers. But they still submit documents at application. Capture reads them and returns decision-ready credit signal in under 30 seconds, so you decide in real time instead of declining.

01

The bureau returns empty

First-time and informal borrowers carry thin or no bureau file. Traditional scoring has nothing to work with, so good borrowers get declined by default.

02

Read the documents they already submit

Bank statements, payslips, IDs, e-wallet records. Capture pulls 500+ signals from the files already sitting in the application, no extra data collection required.

03

Decide in real time

Decision-ready signal lands in your risk model in under 30 seconds. Approve more good borrowers, set limits from verified capacity, and add no risk to the book.

+7.0 Gini

Over the bureau score when a data-rich document is present. In an 8,000-loan backtest across MSME and micro lenders, borrower-submitted documents added +2.4 Gini portfolio-wide and +7.0 on files with bank statements and payslips.

Read the backtest

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.
capture_response.json200 OK
{
"document_type": "bank_statement",
"institution": "BDO Unibank",
"period": "Sep–Dec 2025",
// metrics
"avg_monthly_balance": 142580.50,
"net_inflow_3mo": 485200.00,
"dscr": 2.4,
// qualitative reads
"income_pattern": "stable_recurring",
"unusual_activity": false,
// metadata + fraud
"fraud_score": 0.02,
"tampering_detected": false
}

Any document. Any format. No templates.

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
i.

Python SDK

Install, authenticate, and start processing in under 10 lines. Type hints and async support included.

ii.

REST API

Language-agnostic HTTP endpoints with webhook callbacks. Output in JSON, CSV, Excel, or custom. OpenAPI spec included.

iii.

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.

i.

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.

ii.

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.

iii.

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.

Underwrite more borrowers. Faster. With cleaner data.