Developer API

Bank Statement APIBuilt for AI Agents

Sub-500ms processing • 99%+ accuracy • AI-native JSON responses
Perfect for LangChain, OpenAI GPT Store, CrewAI integrations

<500msProcessing time
99%+Accuracy rate
1000+Bank formats

Simple Integration

import { StatementConverter } from 'statement-converter-sdk';

const converter = new StatementConverter(api_key);

// Process statement
const result = await converter.process({
  file: statement_pdf,
  format: 'ai_optimized' // Token-efficient JSON
});

console.log(result.transactions);
// [{ date: "2024-01-15", amount: -124.50, 
//    description: "Coffee Shop Purchase",
//    category: "food_dining" }]

AI-Optimized API Response

Token-efficient JSON designed for LLMs with standardized fields across all banks

{
  "document_id": "doc_abc123",
  "bank_name": "Chase Bank",
  "account_number": "****1234",
  "statement_period": {
    "start": "2024-01-01",
    "end": "2024-01-31"
  },
  "summary": {
    "opening_balance": 5234.67,
    "closing_balance": 4891.23,
    "total_deposits": 3500.00,
    "total_withdrawals": 3843.44
  },
  "transactions": [
    {
      "id": "txn_001",
      "date": "2024-01-15",
      "amount": -124.50,
      "type": "debit",
      "description": "STARBUCKS STORE #1234",
      "category": "food_dining",
      "merchant": {
        "name": "Starbucks",
        "category": "coffee_shops"
      }
    }
  ],
  "processing_time_ms": 387
}

Ready-to-Use Integrations

Drop-in code examples for popular AI frameworks and automation tools

LangChain Document Loader
from langchain.document_loaders import StatementConverterLoader

# Load and parse bank statements
loader = StatementConverterLoader(
    api_key="your_api_key",
    format="ai_optimized"
)

documents = loader.load([
    "/path/to/statement1.pdf",
    "/path/to/statement2.pdf"
])

# Each document contains structured transaction data
for doc in documents:
    print(f"Bank: {doc.metadata['bank_name']}")
    print(f"Transactions: {len(doc.metadata['transactions'])}")
    
# Use with RAG pipeline
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings

vectorstore = Chroma.from_documents(
    documents=documents,
    embedding=OpenAIEmbeddings()
)

Performance That Matters

Built for production AI applications with enterprise-grade reliability

Processing Speed Comparison
StatementConverter
387ms
DocuClipper
2.1s
Veryfi
3.2s
Mindee
4.8s
Accuracy by Bank Type
Major Banks (Chase, BoA, Wells Fargo)99.7%
Regional Banks99.2%
Credit Unions98.8%
International Banks98.1%
Scanned/OCR Documents96.9%

Format Coverage

1000+
Bank Formats
50+
Countries
99.9%
Uptime SLA
24/7
API Support

Developer Resources

Everything you need to integrate and build with our API

Interactive API Docs

Live API documentation with request/response examples and testing playground.

Coming SoonFull API documentation will be available with public launch
Python SDK

Full-featured Python SDK with async support, webhooks, and batch processing.

Coming SoonPython SDK will be available with public launch
JavaScript SDK

TypeScript-ready SDK for Node.js and browser environments with built-in error handling.

Coming SoonJavaScript/TypeScript SDK will be available with public launch
Code Examples

Ready-to-run examples for LangChain, OpenAI, CrewAI, n8n, and Zapier integrations.

Coming SoonComplete example repository will be available with public launch
Postman Collection

Complete Postman collection with environment variables and test scripts.

Coming SoonPostman collection will be available with public launch
Developer Community

Join our Discord community for support, feature requests, and beta updates.

Coming SoonDeveloper community will be available with public launch
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Developer Benefits

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