RFQ Processor
AI-Powered Automation
A system that extracts and analyzes RFQ data from Outlook .msg and PDF files using GPT, OCR, and PyPDF2.
Role
Full Stack Developer
Duration
3 months
Client
RFQ Processor
Year
2024
Project Overview
The RFQ Processor is an intelligent automation tool designed to streamline the Request for Quotation (RFQ) process. It automatically ingests emails and attachments (PDFs, Excel), extracts critical data using OCR and LLMs, and structures it for downstream processing. This tool significantly reduces manual data entry time and minimizes human error.
Key Features
Core functionalities that make this project stand out
Automated Email Parsing (.msg files)
Intelligent PDF Data Extraction with OCR
GPT-4 Integration for Unstructured Data Analysis
Structured Output Generation (JSON/CSV)
Flask-based API for Integration
Error Handling and Validation Pipeline
Technology Stack
Technologies and tools used to build this project
Frontend
Backend
Tools & Services
Challenges & Solutions
Key obstacles faced and how they were overcome
Challenge
Unstructured Data Formats
RFQs come in various layouts and formats, making rule-based extraction impossible.
Solution
LLM-Powered Extraction
Leveraged OpenAI's GPT models to interpret and normalize data from diverse formats, achieving high accuracy.
Challenge
OCR Accuracy
Scanned PDF documents often have low quality, leading to potential data loss during extraction.
Solution
Hybrid OCR Approach
Combined Tesseract OCR with layout analysis to better reconstruct tables and text blocks before processing.
Project Gallery
Visual showcase of the project
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