Gen AI Knowledge Automation Platform

Problem Statement

An enterprise support organization had scattered documentation and inconsistent knowledge retrieval, leading to long resolution cycles and repeated escalations.

Technical Architecture

We implemented a Python-first Gen AI platform with RAG pipelines, vector indexing, prompt orchestration, and policy guardrails. Kafka handled document ingestion streams, Redis cached semantic retrieval sessions, and Python services exposed governed inference APIs to internal tools.

Technology Stack: Python, FastAPI, RAG, Vector Database, Kafka, Redis, PostgreSQL, OpenAI

Business Transformation

Support and operations teams moved from keyword search to context-aware AI assistance with source-cited responses and controlled model behavior.

Quantifiable Outcomes

• 57% reduction in mean time to resolution for L2 support
• 3.4x increase in knowledge retrieval accuracy on priority intents
• 42% lower escalation volume for repetitive operational issues