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Financial Technology
Amazon Bedrock (Qwen3 235B Vision), AWS Lambda, Amazon API Gateway, Amazon S3, Amazon DynamoDB, Amazon CloudWatch, AWS CloudTrail, AWS IAM
GenAI-powered platform for automated financial document extraction with structured outputs and scalable processing.
The client is a leading India-based portfolio management platform that helps financial advisors and investors track mutual fund investments, analyze portfolio performance, assess risks, and make data-driven investment decisions through real-time insights and analytics.
Reduction in Manual Data Entry Effort
Extraction Accuracy from Complex Financial Documents
Processing Capacity with upto 100 Pages per Document
The client faced challenges in efficiently processing and extracting data from documents due to a largely manual workflow, resulting in delays and inconsistent output quality. Their existing system lacked scalability controls for handling large document uploads, impacting processing reliability and accuracy. Additionally, the cost of third-party extraction tools did not align with the client’s fixed subscription model, and there was no automated solution to transform critical information such as headers, trade details, and levy calculations into structured JSON or CSV formats.
• GenAI Vision-Based Document Extraction using Amazon Bedrock Qwen3 235B Vision model for high-accuracy structured data extraction from financial PDF documents including tables, forms, and complex layouts.
• Intelligent PDF Processing Pipeline with AWS Lambda orchestrating PDF-to-image conversion, parallel page processing, and structured JSON output for header information, trade details, and levy calculations.
• Real-Time Processing Interface via Amazon API Gateway WebSocket connections and AWS Lambda, providing live progress updates during document extraction and supporting both individual and batch processing modes.
• Automated Extraction Workflow with Amazon Bedrock and AWS Lambda for document ingestion, validation, field mapping, and structured response generation with retry logic and exponential backoff.
• LLM-Powered Auto-Matching & Field Normalization enabling automatic mapping of varying field names and character representations across different document formats to a standardized output schema.
• Scalable Cloud-Native Storage with Amazon S3 for raw file and output storage (JSON/CSV), Amazon DynamoDB for extraction metadata and feedback tracking, and organized folder structures for complete audit trails.
• Comprehensive Security & Observability using AWS IAM for role-based access control, Amazon CloudWatch for real-time monitoring, and AWS CloudTrail for complete audit logging.
Delivered a GenAI-powered financial document extraction solution using Amazon Bedrock Vision AI and AWS serverless services, achieving 90%+ reduction in manual effort, 95%+ extraction accuracy, and scalable processing of up to 200 documents per day with continuous feedback-driven optimization.
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