Enabling Secure, Intelligent Access to Company Knowledge through Semantic Search & RAG

Overview

I led the design and delivery of AI-powered solutions that fundamentally redefined how organizations access institutional knowledge. By integrating Retrieval-Augmented Generation (RAG) and agent-based architectures, we unlocked insights trapped in siloed, unstructured enterprise content—transforming legacy documentation, support tickets, and Confluence spaces into actionable intelligence.

The solution empowers service teams, support agents, and business users to ask complex questions and receive accurate, context-aware responses—instantly and securely.

Problem Solved

Enterprise service teams were losing time and efficiency due to:

• Fragmented, outdated, and unstructured knowledge bases

• High onboarding effort due to reliance on undocumented, tribal knowledge

• Brittle keyword search that failed to capture nuance in real-world queries

• Compliance risks from uncontrolled access to sensitive documents

Traditional enterprise search couldn’t scale to meet modern expectations. These organizations needed intelligent, secure, and dynamic access to their internal knowledge.

Solution & Strategic Value

• 50% improvement in search accuracy, surfacing relevant insights instantly

• Preserved institutional knowledge by extracting value from legacy documentation

• Accelerated onboarding and decision-making for support and service teams

• Improved customer satisfaction through accurate, consistent support answers

• Maintained data governance and access control across all knowledge sources

This enabled organizations to treat knowledge as a strategic asset—accessible, reusable, and actionable through natural language.

Key Capabilities Delivered

• Intelligent Chatbot Interface: A conversational front-end that understands domain-specific context and returns reliable, explainable responses.

• Agent-Based RAG Architecture: Multi-step reasoning agents that plan and execute complex queries—moving beyond simple retrieval.

• Semantic + Hybrid Search: Integrated vector databases for meaning-based document matching and hybrid retrieval pipelines.

• External API Integration: Combined internal and public knowledge sources for comprehensive, up-to-date information access.

• Enterprise-Grade Security: Guardrails, access control, and prompt hygiene for safe, compliant responses.

Business Impact

• +50% improvement in information retrieval accuracy vs. legacy search tools

• Reduced support query resolution time via task-specific reasoning agents

• Lowered onboarding time for new hires by making legacy knowledge discoverable

• Boosted service team efficiency and confidence through self-service AI

• Improved CSAT with faster, more accurate, and consistent customer support

My Role & Leadership

• Architected and led implementation across PoCs for multiple enterprise clients

• Defined end-to-end agent-based RAG pipelines, integrating structured and unstructured data

• Championed user-centric design for intuitive adoption by service and support teams

• Oversaw compliance, performance, and reliability optimizations through guardrails and evaluation frameworks

Technical Highlights

• Vector Databases: Vertex AI Matching Engine, Pinecone, Weaviate, Elasticsearch

• LLMs: GPT-4, Gemini Pro, Claude

• Frameworks: LangChain, LangGraph, NeMo Guardrails

• Frontend: Streamlit-powered chatbot UIs

• Infrastructure: Google Cloud Platform (Vertex AI, Cloud Run, BigQuery)

• Languages & Tools: Python, Docker, GitHub Actions

Why It Matters

This project marks a fundamental shift from static documentation to dynamic, AI-native knowledge systems—turning scattered, hard-to-maintain knowledge bases into scalable, intelligent assistants. It directly enhances the productivity of human teams while preparing the organization for future agent-driven automation.