AI Integration

Enterprise Knowledge & Semantic RAG System

Custom Vector Search & Retrieval Layer for Internal Documentation

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Enterprise Knowledge & Semantic RAG System

Executive Overview

A custom vector search and retrieval-augmented generation (RAG) system embedded directly into an internal corporate portal, enabling instant natural language search across thousands of technical SOPs.

The Challenge

Operational Problem & Inefficiency

Engineers and support personnel struggled to locate relevant operating procedures and historical troubleshooting guides stored across disparate wiki pages, shared drives, and PDF repositories.

The Solution

Engineered AI Architecture & Execution

Built a secure vector embedding pipeline with hybrid keyword-semantic search and citation-backed answer generation embedded directly into the company's existing intranet portal.

Key Deliverables

Continuous document ingestion & chunking pipeline
Vector database cluster setup and index optimization
Internal query web component for intranet embedding
Role-based access filtering layer

Business Value Delivered

Instant, cited answers extracted directly from verified company documentation
No disruption or replacement required for existing document storage systems
Strict access control ensuring users only query documents matching their permission tier
Accelerated onboarding and faster internal issue resolution

Technology Stack

TypeScriptPythonQdrant Vector DatabaseFastAPINext.jsTailwind CSSEmbeddings API

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