Internal Knowledge Navigator

Service Description

Overview: We will design a secure, AI-powered internal knowledge assistant that helps employees find and use company documents (e.g., onboarding guides, policies, contracts, meeting notes) without sending data outside your systems. Built on a private LLM (like ChatGPT) combined with Retrieval-Augmented Generation (RAG), it delivers fast, accurate answers grounded in your internal content. Scope of Work: 1. Discovery & Scoping – Identify key use cases (onboarding, compliance, support) – Map document sources (SharePoint, Drive, Confluence) – Review access, permissions, and privacy needs 2. Data & AI Readiness – Audit document quality and structure – Assess security and deployment options – Recommend LLM model (e.g. Azure OpenAI, PrivateGPT) 3. Solution Architecture – Design RAG pipeline (ingestion, tagging, indexing) – Define LLM hosting, integrations, and governance – Provide implementation roadmap and estimate Deliverables: – Use case and data map – LLM + RAG architecture and tech stack – Privacy & compliance summary (aligned with your legal & compliance team) – Implementation plan (phases, tools, timeline) Duration: 2–4 weeks Format: Virtual workshops + documentation (Potential onsite kick-off)