AI-Powered Document Intelligence Platform
Enterprise Client
The Problem
A large enterprise was spending thousands of person-hours per month manually reviewing, classifying, and extracting information from complex documents — contracts, regulatory filings, and compliance reports. The existing keyword-based automation caught only 20% of relevant content and produced too many false positives to be useful.
Our Approach
We built a RAG (Retrieval-Augmented Generation) pipeline that combines semantic search over document embeddings with LLM-powered extraction and classification. The system was designed for accuracy first — with human-in-the-loop verification for high-stakes decisions and confidence scoring that routes uncertain cases for manual review.
The Solution
The deployed platform processes documents through a multi-stage pipeline: OCR and preprocessing, semantic chunking, embedding and indexing, LLM-powered extraction with structured output, and confidence-based routing. A comprehensive evaluation framework tracks accuracy, recall, and drift over time.
Results
90%
Reduction in manual review
97%
Extraction accuracy
10K+
Documents processed daily
3 weeks
From prototype to production