Smart Automated Bill Data Extraction System

Overview

JashDS developed an automated bill data extraction system for a financial services company that replaced Finovera API limitations with AWS Bedrock's LLM capabilities, achieving over 95% accuracy in extracting complex billing information from unstructured PDF and image documents. The serverless solution successfully extracted additional custom fields unavailable through existing systems while processing 3,000 bills at approximately $30-$40 cost through an optimized three-Lambda architecture with Aurora PostgreSQL storage and comprehensive SQL-based validation workflows.

About the Client

A financial services company providing bill payment and account management solutions through their proprietary Finovera API platform, requiring enhanced data extraction capabilities from unstructured bill documents.

The Challenge

The client faced significant limitations with their existing bill data extraction system that relied on Finovera API. The system struggled to extract complex textual information such as late fee descriptions and detailed charge descriptions from PDF and image-format bills. Additionally, Finovera API lack of customization capabilities prevented the extraction of client-specific fields and unique metadata, resulting in incomplete data capture for critical billing analysis and downstream processing workflows.

Key Results

Achieved over 95% accuracy rate in automated bill data extraction through LLM-based validation against Finovera API data.
Successfully extracted additional custom fields from bills that were previously unavailable through the existing Finovera system, enriching the dataset for comprehensive analysis.
Reduced processing costs to approximately $30-$40 for processing 3,000 bills through optimized AWS serverless architecture.

Our Solution

JashDS implemented a comprehensive serverless solution leveraging AWS Bedrock's advanced Large Language Model capabilities to replace the limitations of Finovera API. The team developed a three-Lambda architecture where the first Lambda function processes uploaded bills from S3 buckets using AWS Bedrock for intelligent data extraction, the second Lambda function stores the structured data in Aurora PostgreSQL, and the third Lambda handles validation workflows.

The solution included the creation of a robust database schema with over 20 fields including provider information, account details, payment methods, billing periods, and token usage metrics. A sophisticated SQL-based validation process was implemented to compare LLM-extracted data with existing Finovera API data, calculating column-level matches and percentage scores for accuracy measurement.

The team established an automated workflow triggered by S3 file uploads, ensuring seamless processing of both PDF and image format bills. Advanced error handling, logging mechanisms, and token counting features were integrated to optimize performance and cost management across the entire data extraction pipeline.

Technologies Used

AWS Bedrock (Large Language Models)AWS Lambda (Serverless Computing)Amazon S3 (Document Storage)Amazon Aurora PostgreSQL (Database)AWS Textract (Initial Implementation)Amazon DynamoDB (Alternative Storage)SQL (Data Validation)Python (Lambda Functions)AWS RDS (Database Management)JSON (Data Serialization)

Related Case Studies

← Back to All Case Studies

Data Science

AI-Powered Dynamic Workout Scheduling System

JashDS implemented an AI-powered workout personalization system that dynamically adapts to individual user patterns and training history, delivering intelligent gap analysis and muscle group imbalance correction for 10,000+ users across 38 time zones. The solution leveraged Claude 3.5 Sonnet AI to analyze previous workout completions, detect missing muscle groups, and generate personalized schedules that prioritize corrective training while maintaining optimal strength-cardio-core hierarchy based on each user's unique constraints and availability.

Data Science

AI-Assistance for Customer Support for Rent-to-Own Industry

A rent-to-own industry organization struggled with inconsistent customer support quality and slow response times that impacted lead conversion rates. By implementing an AI-powered chat assistance system using AWS Bedrock and retrieval-augmented generation, the organization enabled agents to receive three context-aware response suggestions within seconds during live conversations. The solution leverages historical successful conversations through semantic search and Claude Haiku 4.5, ensuring every agent delivers high-quality, proven communication strategies regardless of experience level. The serverless architecture processes thousands of requests monthly while maintaining reliability through intelligent fallback mechanisms and comprehensive monitoring.

Data Science

Artificial Intelligence - Driven Candidate Screening Revolution

JashDS revolutionized a company's hiring process by developing a GenAI-powered candidate screener that reduced time-to-hire by 50% and improved hiring outcomes. The solution leverages advanced language models to conduct dynamic, role-specific interviews, automatically generating and adapting questions based on job descriptions and candidate responses.

Have a similar challenge?

Connect with us