AI WORKSPACE
Enterprise document intelligence. Chat with thousands of PDFs instantly with exact source citations.
Role
AI Integration
Platform
Web App SaaS
Timeline
10 Weeks
Core Stack
OpenAI, Pinecone
The Challenge
Legal and compliance teams were spending roughly 40% of their work week simply searching for specific clauses across fragmented, massive PDF repositories.
When ChatGPT launched, the client tried pasting documents in, but hit context token limits immediately. They needed an enterprise-grade solution that could "read" a library of 10,000+ internal rules, policies, and contracts, and accurately answer questions based strictly on that proprietary data, without hallucinating.
The system required granular role-based access control (RBAC), preventing junior analysts from querying highly confidential executive documents.
The Architecture
We constructed a secure Retrieval-Augmented Generation (RAG) pipeline using a specialized vector database and custom chunking algorithms.
Semantic Vector Search
Documents are parsed, chunked contextually, converted into high-dimensional embeddings using OpenAI models, and stored in Pinecone for instant similarity retrieval.
Enterprise Guardrails
We implemented strict prompt engineering and system prompts to force the LLM to only answer from retrieved context, providing exact page-number citations, entirely eliminating hallucinations.
Engineering Impact
The workspace transformed how the firm interacted with their unstructured data, turning a massive liability into an instant knowledge graph.
Query Speed
Average time from natural language question to cited answer.
Documents
Successfully ingested, embedded, and actively searchable.
Hallucination Rate
In production, thanks to aggressive retrieval constraints.