KE RAG chatbot

Designed and deployed a local RAG knowledge engine for a major Pakistani utility enterprise, cutting internal technical support workload by 80% by making thousands of pages of operational documentation instantly queryable.

K-Electric's field operations teams were losing hours daily manually searching through dense installation manuals and repair guides. At enterprise scale, that translates to thousands of productive hours lost to document hunting every month.

A fully local, offline-capable knowledge retrieval system using Python, LangChain, Llama 8B, and a Next.js control interface - designed to run on K-Electric's internal infrastructure without any data leaving the network.

RAG Pipeline: Chunked, vectorized, and stored large operational field manuals using LangChain, enabling semantic search across thousands of pages of operational documentation

Local LLM Inference: Integrated a locally hosted Llama 8B model to generate document-aware answers from retrieved context - field staff ask natural language questions and get specific, document-referenced answers in seconds

Async API Gateway: Built a fast Uvicorn-based Python service layer connecting the LLM backend to the Next.js frontend with low-latency response handling

Delivered a proven 80% reduction in technical support queue workload. Field staff went from multi-hour documentation searches to accurate answers in under 30 seconds - entirely on local infrastructure with no cloud dependency or data exposure risk.