Notes on keeping what your company knows
Practical writing on knowledge retention, offboarding and governing enterprise AI, by the people building MindKeepr. 22 articles from 3 authors.
AI agent governance: a checklist for the questions security will ask
Agents act, they do not just answer. Here are the controls that decide whether a security team approves a deployment: scoped identity, an approved tool list, authority checks, stopping points, and a record of every run.
A knowledge transfer plan template that people actually finish
Four columns, one rule, and a way to find the items that matter. A practical knowledge transfer plan for a departure, a role change, or a project handover, with the format and the questions to use.
Institutional knowledge vs tribal knowledge, with examples
The two terms are used interchangeably and should not be. One is what an organisation holds, the other is what a specific group holds and nobody wrote down. The difference tells you which one to go after first.
Why we built an AI that refuses to answer
Most AI tools answer everything, including the questions nobody in your company has ever answered. Here is the case for a system that stops, names what is missing, and asks a person instead.
Approval is not authority: what an audit actually asks
Every AI workflow tool has an approval step. Few can answer the question an auditor asks next: was that person allowed to decide, and does the approval still cover the text in front of me?
One document, two systems, one permission model
The same file arrives from two tools. Storing it twice makes answers drift. Merging it carelessly can show somebody a document they were never given access to. Here is how we handle it.
The questions your service desk keeps asking, and nobody answers
The same question arriving five times in a fortnight is not a training problem. It is the cheapest signal you will ever get that your company has never written something down.
What a handover misses when someone leaves
A good handover captures documents, access and open work. The thing that actually leaves with the person is the reasoning behind the decisions they made, and no checklist asks for it.
Why knowledge management matters, and AI makes it urgent
Knowledge management turns scattered, tribal know-how into governed organisational memory your people and your AI can use. Here is why it matters now.
The real cost of losing an employee's knowledge (and how to keep it)
When an employee leaves, the documents stay but the reasoning walks out the door. Here is what that actually costs, why wikis and search do not fix it, and how to retain the knowledge instead.
What is knowledge retention? (and how it differs from knowledge management)
Knowledge retention is how organisations keep what their people know when they leave. Here is a clear definition, why it matters, and how it differs from knowledge management.
How to choose knowledge management software (a practical checklist)
A practical, vendor-neutral checklist for choosing knowledge management software: the categories, the criteria that matter, the questions to ask, and the red flags to avoid.
A knowledge retention strategy that actually works (with examples)
A practical knowledge retention strategy: identify at-risk knowledge, capture it before people leave, preserve it in a usable form, and govern access. With real examples.
Employee offboarding knowledge-transfer checklist
A practical offboarding checklist to transfer an employee's knowledge before they leave, so the team keeps the context, not just the files.
How to prevent knowledge loss when employees leave
Practical ways to prevent knowledge loss when employees leave: what to do before, during, and after a departure to keep critical context inside the company.
How to capture tacit knowledge (the stuff in people's heads)
Tacit knowledge is the hardest to keep and the most valuable. Here is what it is, why it resists documentation, and practical ways to capture it.
AI knowledge management, explained
What AI knowledge management is, how it works, and the governance pitfalls to avoid. A clear explainer for teams adopting AI on top of their knowledge.
Internal knowledge base: build vs buy
Should you build your own AI knowledge base or buy one? A practical look at the real costs of building RAG infrastructure versus adopting a governed platform.
Knowledge management for IT and DevOps teams
IT and DevOps teams lose critical knowledge in incidents, runbooks, and the heads of senior engineers. Here is how to capture and keep it.
Enterprise AI readiness: a practical framework
Most AI initiatives stall on knowledge, not models. A practical framework to assess and improve your enterprise AI readiness across governance, permissions, and freshness.
Why your AI needs a knowledge layer (RAG and MCP)
AI without your context guesses. A knowledge layer gives every AI tool governed, current, permission-aware access to what your company knows, through RAG and MCP.
Confluence and AI: where it helps and where it falls short
Confluence is great for authoring docs, and its AI helps within Atlassian. Here is where it falls short for cross-tool knowledge and retention, and how to fix it.