Enterprise SaaS AI-Native Full-Stack

AI-Powered Knowledge
Base Platform

Built an enterprise-grade AI knowledge repository that replaces fragmented documentation and tribal knowledge with instant, intelligent retrieval. Using vector embeddings and large language model retrieval, teams across Engineering, Operations, and Support can surface exact answers from thousands of documents in milliseconds — reducing onboarding time, cutting support ticket volume, and eliminating information silos across the organisation.

10x Faster Information Retrieval

60% Reduction in Support Queries

85% Team Adoption Rate

3 Departments Integrated

Data & Outcomes

Performance & Adoption Metrics

Search Response Improvement (Week 1 → 12)

Team Adoption by Department

Engineering (35%)

Operations (28%)

Support (22%)

Others (15%)

Engagement Phases

How We Built It

Phase 1 — Foundation

Architecture & Data Ingestion Pipeline

Designed the core platform architecture with vector database integration. Built a multi-format document ingestion pipeline (PDF, DOCX, Markdown, Confluence, Notion) with chunking strategies optimised for retrieval accuracy. Set up role-based access control to ensure teams only see content within their permission scope.

Phase 2 — AI Core

Semantic Search & LLM Retrieval Layer

Implemented vector embeddings for all indexed documents using a fine-tuned embedding model. Built a retrieval-augmented generation (RAG) pipeline with an LLM response layer that synthesises answers from retrieved document chunks. Implemented hybrid search (vector + keyword) for highest recall across both technical and non-technical queries.

Phase 3 — Interface & Deployment

User Interface, Integrations & Analytics

Built a clean, full-text search interface with conversational follow-up capability. Integrated with Slack and internal ticketing systems so teams can query the knowledge base without switching context. Deployed an analytics dashboard tracking query trends, unanswered question gaps, and content performance — enabling continuous improvement of the knowledge base.

What We Delivered

Key Deliverables

Semantic search engine with vector embeddings & LLM retrieval (RAG)

Real-time document indexing pipeline with multi-format support

Role-based access control & permission management system

Analytics dashboard — query trends, gaps & content performance

Slack & ticketing system integration for in-context search

Conversational follow-up interface for multi-turn queries

Hybrid search (vector + keyword) for maximum recall accuracy

Team onboarding guide & content governance documentation