Important knowledge sits in documents, chats, and individual memory.
Case study
AI Knowledge Assistant
Policies, SOPs, client context, and past decisions are scattered.
Operating problem
What keeps breaking.
Teams ask the same operational questions repeatedly.
Answers lack source context.
Sensitive actions need review before execution.
Build model
The work becomes a clearer system.
Structure source material, retrieve relevant context, and answer with controlled workflow guidance.
Operating path
Sources are selected, structured, retrieved, answered from, and reviewed before sensitive use.
AI/product layer
Retrieve grounded context, explain uncertainty, and draft a controlled answer.
Reusable improvement
Repeated questions improve source structure, answer rules, and review guidance.
Workflow
The sequence becomes explicit.
Sources selected
Knowledge structured
Question asked
Context retrieved
Answer drafted
Human review applied
Capabilities
What the system needs.
Source indexing
Controlled retrieval
Answer grounding
Escalation rules
Usage logs
Human review
Connected tools
Where the system fits.
Google DriveSharePointNotionCRMInternal docsAI model
Implementation path
Less dependency on one person's memory.
Step 1
Identify source material
Step 2
Define allowed answers and review gates
Step 3
Build retrieval and response flow
Step 4
Test with real team questions
Explore AI Knowledge Assistant
Tell us where this work currently breaks. We will map the first useful AI/product build path.