Enterprise Knowledge Copilot
A 500-person management consulting firm had accumulated 200K+ documents across SharePoint, Confluence, internal wikis, and shared drives over 15 years. We built a RAG-powered knowledge assistant that turned this scattered institutional knowledge into an always-available, citation-backed AI copilot.
Consultants spent an average of 2.3 hours per day searching for relevant frameworks, past deliverables, and institutional knowledge. The firm's knowledge was scattered across five different platforms with inconsistent naming conventions, outdated content mixed with current material, and no unified search. Senior consultants hoarded knowledge in personal folders that new hires couldn't access.
New hire onboarding was particularly painful — it took 6+ months for junior consultants to become productive because learning the firm's methodologies, case precedents, and client-specific context required navigating a labyrinth of disconnected systems. The firm estimated this knowledge friction cost them $4.2M annually in lost productivity and duplicated work.
We built a RAG-powered knowledge assistant with three key differentiators. First, domain-adapted embeddings: we fine-tuned an embedding model on 5,000 query-document pairs harvested from the firm's existing search logs, improving retrieval precision by 23% over off-the-shelf models on consulting-specific terminology.
Second, hierarchical access control that respects document permissions down to the paragraph level. The system integrates with Azure AD to inherit the firm's existing permission model — a junior analyst and a senior partner asking the same question get different answer contexts based on what documents they're authorized to access.
Third, a citation engine that traces every answer back to source documents with page-level references. Every generated response includes numbered citations that link directly to the source material, so consultants can verify claims and dive deeper. The system integrates natively with Slack and Microsoft Teams, supporting natural language queries in the tools people already use.
Knowledge search time dropped by 87% — from an average of 2.3 hours to 18 minutes per day per consultant. The system processes 12,000+ queries monthly with 94.2% answer accuracy, measured through weekly human evaluation by senior knowledge managers.
New hire onboarding time was reduced from 6 months to 6 weeks. The copilot effectively gives every new consultant access to the firm's full institutional knowledge from day one. Staff adoption reached 92% within the first quarter, and the firm achieved full payback on the implementation investment within 3 months.