Enterprise Knowledge Assistant
A RAG-based assistant deployed across a 2,000-employee enterprise to cut internal search time.
78%
faster knowledge retrieval
The Problem
An enterprise with over 2,000 employees struggled with fragmented internal documentation. Staff spent hours searching through PDF manuals, old wikis, and chat threads to answer policy and product questions, impacting overall operational efficiency.
The Solution
We engineered a secure, production-grade Retrieval-Augmented Generation (RAG) assistant. The pipeline parses, cleans, and indexes thousands of documents using an hybrid vector database. An LLM acts as the reasoning engine, providing grounded answers backed by specific source citations.
Engineering Challenges
Handling complex multi-column tables in financial PDFs, preventing hallucinations on legal clauses, and ensuring sub-second response times across high-volume user query streams.
Outcome & Performance
Reduced internal query lookup times by 78%, saving thousands of working hours monthly. Achieved 98% accuracy in fact extraction and positive feedback across human-in-the-loop review teams.
Architecture Pattern
PDF Preprocessing Pipeline
Integrated component of the system stack.
pgvector Vector Store
Integrated component of the system stack.
Hybrid Sparse-Dense Retrieval
Integrated component of the system stack.
Cohere Rerank
Integrated component of the system stack.
GPT-4o Inference
Integrated component of the system stack.
Technology Stack
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