Intelligent Document Processor
AI-powered data extraction from invoices, contracts, and forms
An enterprise client had a team of 12 people manually extracting data from invoices, contracts, and compliance forms — a slow, error-prone process costing hundreds of hours per month.
The Challenge
An enterprise client had a team of 12 people manually extracting data from invoices, contracts, and compliance forms — a slow, error-prone process costing hundreds of hours per month. They needed an automated system that could handle diverse document formats, extract structured data accurately, and improve over time through feedback loops.
Our Solution
We built a document processing pipeline that combines OCR with large language models to extract structured data from unstructured documents. Documents are uploaded to AWS S3 and processed asynchronously via Celery workers. The OpenAI API extracts key fields (amounts, dates, parties, clauses) with confidence scores. A React-based review interface lets operators verify and correct extractions, feeding improvements back into the system. The pipeline handles invoices, contracts, and compliance forms with format-specific extraction templates.
The Results
Reduced document processing time by 90% — from 15 minutes to 90 seconds per document
Extraction accuracy improved from 78% at launch to 96% through feedback-driven refinement
Saved the client an estimated $180K annually in manual data entry labor
Technical Approach
OpenAI's GPT-4 was chosen for its strong performance on structured data extraction tasks across diverse document formats. Celery workers process documents asynchronously to handle burst uploads without blocking the API. AWS S3 with pre-signed URLs provides secure, scalable document storage. The feedback loop stores correction data and uses it to refine extraction prompts, achieving continuous accuracy improvements.
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