AI-Powered Document Processing
Built an intelligent document processing pipeline that reduced manual review time by 85%
85% reduction in manual processing time
95% extraction accuracy after 2 weeks
3x document throughput
The Problem
A growing SaaS company was drowning in document review. Their team spent 200+ hours monthly manually extracting data from contracts, invoices, and compliance documents.
Our Approach
We designed a multi-stage AI pipeline combining OCR, LLM-based extraction, and human-in-the-loop validation. The system learns from corrections, improving accuracy over time.
Tech Stack
The Challenge
The client's operations team was spending the majority of their time on repetitive document processing. As they scaled, this bottleneck threatened to limit growth.
Our Approach
We built a modular pipeline that could handle various document types:
- Ingestion Layer - Multi-format document intake with automatic classification
- Extraction Engine - GPT-4 powered extraction with structured output schemas
- Validation Queue - Human review interface for edge cases
- Learning Loop - Continuous improvement from operator feedback
Technical Implementation
The system runs on serverless infrastructure for cost efficiency at scale. Documents flow through the pipeline asynchronously, with results delivered via webhook.
Results
The system now processes thousands of documents monthly with minimal human intervention. The client's ops team focuses on exceptions rather than routine extraction.