Rapyder’s AI-Powered Intelligent Document Processing Helped a FinTech Startup Cut Billing Time by 90%

About Customer: 

Customer is a Guntur (Andhra Pradesh)-based AI-driven startup building a voice- and vision-enabled billing and accounting assistant for small businesses. The platform lets SME owners create invoices, scan bills, and retrieve financial reports through simple voice commands or document uploads, with multilingual support and integrations. 

Industry: 

FinTech/SME Accounting Automation 

Offering: 

AWS Bedrock + Generative AI + Intelligent Document Processing (IDP) 

Business Challenges: 

1. Document layout variability – invoices arrived from hundreds of different vendors, each with unique templates, column arrangements, and field labels that broke rule-based extractors. 
2. Mixed document quality – roughly 40% of uploads were scanned images rather than digital PDFs, introducing OCR noise and partial-extraction risk. 
3. Strict database schema requirements – raw OCR text was insufficient on its own; it had to be normalised into structured JSON that mapped exactly to customer’ Aurora MySQL schema (vendor details, item-wise amounts, taxes, totals). 
4. Tight accuracy and speed targets – the solution needed to return structured data within 10–15 seconds per document at 95%+ field-level accuracy, at a volume of 500+ documents per day. 
5. Multi-tenant data security – sensitive financial documents required logical isolation per tenant, from storage through to the database, with credentials and data properly encrypted. 
6. Handwritten content – handwritten annotations on receipts and vouchers risked extraction errors if processed the same way as printed documents. 

Solution Implemented: 

1. Layout-agnostic extraction – Amazon Textract’s layout-agnostic OCR combined with Amazon Bedrock (Claude Sonnet 4) semantic reasoning handles diverse vendor templates without manual configuration or field mapping. 
2. Multi-mode OCR with noise-aware prompting – Textract processes both digital PDFs and scanned images, while prompt engineering in the downstream Bedrock Lambda accounts for OCR noise during semantic structuring. 
3. Schema-aligned prompt design – a detailed schema review with customer informed the Bedrock prompt design, ensuring the generated JSON aligns exactly with Aurora MySQL table and column structure before it is written via encrypted CRUD APIs. 
4. Serverless, event-driven pipeline – an S3-triggered Lambda chain (OCR Lambda → DynamoDB → Bedrock Lambda) using ARM-based Lambda functions minimises cold-start latency and cost, meeting the 10–15 second processing target at scale. 
5. Tenant-segmented, encrypted architecture – tenant-wise nested S3 folders plus API-layer tenant_id/user_id enforcement isolate every customer’s data; AWS Secrets Manager, encryption at rest, and VPC-secured APIs protect credentials and financial data end-to-end. 
6. Confidence-based review routing – a flagging mechanism uses Textract confidence scores to route documents with handwritten content to manual review instead of forcing fully automated extraction. 

Services Used: 

• Amazon Bedrock (Claude Sonnet 4) 

• Amazon Textract 

• AWS Lambda 

• Amazon S3 

• Amazon DynamoDB 

• Amazon Aurora MySQL 

• Amazon API Gateway 

• AWS Secrets Manager 

• Amazon CloudWatch 

• AWS CloudTrail 

• AWS IAM 

Business Benefits: 

1. Manual errors eliminated – near zero for standard formats, as AI-driven field classification replaces brittle rule-based extractors across hundreds of vendor layouts. 
2. 95%+ field extraction accuracy – the target accuracy for vendor, amounts, taxes, and totals is met even with ~40% of documents arriving as scanned images. 
3. ~90% billing time reduction – structured JSON maps directly into Aurora MySQL, eliminating manual data entry and re-keying for SME customers. 
4. 10–15 second processing per document – supporting a 100,000+ documents/month capacity on a fully serverless, auto-scaling architecture. 
5. 100% logical multi-tenant isolation – with a full audit trail via CloudWatch and CloudTrail, and zero credentials stored outside Secrets Manager, meeting financial data security requirements. 
6. Safe handling of edge cases – handwritten and low-confidence documents are automatically routed for review, keeping automation accuracy high without sacrificing data integrity.

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