Objective and Scope:

Developed an enterprise-grade Knowledge Center that enables organizations to unlock value from large volumes of internal documents through AI-powered search and chat. The client wanted a platform that leverages LLM frameworks, cloud‑native infrastructure, Retrieval‑Augmented Generation (RAG), and intelligent tagging and enrichment to create a single, trusted source of contextual answers grounded in their own data.

Approach:

The platform was designed as a scalable, cloud‑native full‑stack web application that sits on top of the client’s existing knowledge repositories. Different types of documents are ingested, cleansed, and enriched with intelligent tags, then indexed to power RAG‑based retrieval and AI search. On this foundation, an AI chat interface powered by LLM to interpret user queries, retrieve the most relevant passages, and generate accurate, context‑aware responses that remain grounded in the organization’s own documents and security model. Followed MLOps practices with continuous monitoring and evaluation of model performance.

Impact:

This system helped the client in the following:

• Consolidate fragmented research into a single, governed source of truth, preserving institutional knowledge
• Transform deliverables into dynamic, queryable assets to detect regulatory shifts and competitor actions early
• Establish a governed ML lifecycle with continuous monitoring and version control, ensuring consistent performance and reduced operational risk