Series
The knowledge layer under every enterprise AI workflow: turning scattered documentation into usable context, the debt that surfaces when you try, and a format agents can rely on.
Every useful enterprise AI workflow eventually reaches for internal context — policies, product docs, project history, the reasoning behind decisions nobody wrote down. That is where most of them stop. The model is fine; the knowledge underneath it is missing, stale, duplicated, or owned by nobody.
This series follows that thread from three angles: a workflow for synthesising scattered documentation into something structured, what connecting AI to internal knowledge reveals about the debt already there, and a concrete markdown-and-git format that makes knowledge agent-ready rather than merely searchable.
Part 1 of 3 ·
Stop drowning in fragmented data and start building actionable intelligence. Learn how a specialized AI workflow can transform scattered documentation into a structured "source of truth," moving beyond simple search to enable deep organizational reasoning.
Part 2 of 3 ·
Why connecting AI to internal knowledge surfaces missing, stale, duplicated, and unowned context, and why the real work is fixing the knowledge system around the model.
AI does not magically clean up messy organizational knowledge. It exposes where context is missing, stale, duplicated, or owned by nobody.
Part 3 of 3 ·
A hands-on Open Knowledge Format setup: markdown, git, an agent skill, pull requests, and Obsidian for trustworthy AI context.
A hands-on Open Knowledge Format setup: a markdown wiki with defined domains, an AI skill that turns raw content into PRs, and a link graph in Obsidian.