Enterprise AI Knowledge Engine
Unified querying across fragmented enterprise data
66%
Faster review
20h/wk
Time saved
4 weeks
Time to deploy
The Problem
Management spent 20+ hours weekly manually reviewing scattered documents across multiple internal systems. Critical insights were buried in unstructured PDFs, emails, and legacy databases, making informed decision-making slow and unreliable. Non-technical stakeholders had no way to query this data without developer involvement.
The Solution
Built a RAG pipeline with a robust ETL layer that ingests, chunks, and indexes all internal documents regardless of format. Combined this with a chat-based query interface that returns cited answers, allowing management to ask natural language questions and get accurate, sourced responses in seconds.
How it works
1/2Data ingestion and normalization pipeline — handling PDFs, JSONs, emails, and legacy database exports through a unified ETL process
Tech Stack
What I'd do next
Add real-time document sync so newly uploaded files are indexed within minutes. Expand to multi-modal inputs including images, scanned documents with OCR, and video transcripts. Implement role-based access controls for sensitive data compartmentalization.