Enterprise RAG Knowledge Management System
Enterprise knowledge management system powered by RAG architecture. Unified search across documents, wikis, ticketing systems and email — delivering synthesised answers with citations.
Multi-division enterprise — corporate services, operations and field teams
A large WA enterprise with 500+ staff across corporate offices, operational sites and field teams. Knowledge was distributed across SharePoint (documents), Confluence (wiki), Jira (project history), Outlook (correspondence), a legacy intranet and multiple shared drives.
Staff averaged 1.8 hours per day searching for information — checking multiple systems, emailing colleagues, or recreating information that already existed somewhere. New employee onboarding took 2–3 months because of the fragmented knowledge landscape.
What needed to change
Knowledge was siloed across 6+ systems. Each department used different tools. Operations had SharePoint and shared drives. IT used Confluence and Jira. HR had a legacy intranet. Executive communications lived in email. No single system could search across all of them.
Search was keyword-based and ineffective. Staff who searched within individual systems got irrelevant results or nothing at all. A search for "procurement approval process" might return 200 documents — most outdated or irrelevant. People stopped searching and started asking colleagues instead.
Institutional knowledge was draining away. Retirements and resignations at senior levels meant decades of accumulated knowledge left the organisation. No system captured the context, reasoning and lessons learned that experienced staff carried in their heads.
What we built
An enterprise RAG knowledge management system that indexes content from all organisational sources, synthesises natural language answers from relevant documents, and provides cited sources for verification.
Multi-Source Ingestion
Automated connectors for SharePoint, Confluence, Jira, shared drives and email archives. Documents processed, chunked and embedded regardless of source format. Incremental sync keeps the index fresh.
Semantic Search Engine
Natural language queries return synthesised answers — not just document links. The system understands intent, not just keywords. "How do we handle contractor safety inductions?" returns a clear, actionable answer.
Source Citations & Verification
Every answer cites the source documents, sections and dates. Users can click through to verify any claim. Trust levels indicated based on document currency and authority.
Access Control Layer
Role-based access control mirrors source system permissions. Users only see answers derived from documents they have access to. Sensitive documents excluded from general search results.
How it works
User asks a question
Types a natural language query in the search interface — "What is the approved vendor list for electrical contractors?" or "What were the outcomes of the 2023 safety review?"
System retrieves from all sources
Semantic search runs across the entire indexed corpus — documents, wiki pages, project records and archived communications. Most relevant chunks retrieved regardless of source system.
AI synthesises the answer
GPT-4 generates a clear, structured answer from the retrieved content. Information from multiple sources is woven together into a coherent response. Constrained to source material.
Citations link to originals
Each claim in the answer cites the source — document name, section, date and system of origin. User clicks to open the original in its native application.
Related knowledge surfaced
Alongside the answer, the system shows related documents, wiki pages, project records and communications. Users discover relevant knowledge they did not know existed.
Measurable outcomes
Our staff were spending almost 2 hours a day looking for information. Now they ask a question and get an answer in seconds — with links to the source documents. It has fundamentally changed how our organisation accesses knowledge.
How we delivered it
Knowledge Landscape Audit
2 weeksMapped all knowledge sources across the organisation. Assessed content volume, format, access controls and currency. Identified the highest-value knowledge categories and most common search scenarios.
Connectors & Ingestion
4 weeksBuilt automated connectors for each source system. Designed the chunking and embedding strategy optimised for enterprise content. Processed the initial 50,000+ document corpus.
RAG Engine & UI
4 weeksBuilt the retrieval and generation pipeline with access control, citation linking and answer confidence scoring. Developed the search interface with source filtering, topic navigation and feedback mechanisms.
Pilot & Enterprise Rollout
2 weeksPiloted with 3 departments for 4 weeks. Refined retrieval quality, answer formatting and access controls based on real usage. Rolled out enterprise-wide with department-specific onboarding sessions.
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