Consulting — Knowledge System Audit
Understand what you have
before you fix anything.
Most organizations that come to me with a knowledge problem do not know exactly where it starts. They know something is broken — employees can’t find answers, AI tools return wrong content, onboarding takes too long. The audit maps the ecosystem so the root cause is clear before any work begins.
What this engagement addresses
Knowledge systems break down in predictable ways: content is duplicated across repositories, taxonomy is absent or inconsistent, governance exists on paper but not in practice, and the architecture was never designed for how the organization actually uses information. The audit surfaces all of it.
This is the right starting point if your organization is dealing with any of the following:
Failed AI retrieval
Your enterprise AI tools return inconsistent or outdated answers because the content feeding them was never structured for machine consumption.
Tribal knowledge dependency
Critical knowledge lives in the heads of specific employees rather than in accessible, structured systems. Turnover creates operational risk.
Post-merger content fragmentation
Two organizations have merged and brought their documentation systems with them. Nobody has a clear picture of what exists where, or what conflicts.
Pre-transformation baseline
You are planning a knowledge management initiative and need a rigorous assessment of current state before committing to a direction.
How the audit works
Inventory and discovery
Systematic mapping of all content repositories, systems, and informal knowledge sources. Where does content live, who owns it, how old is it, and how is it currently accessed?
Structural assessment
Evaluation of taxonomy, metadata, content types, and governance mechanisms currently in place. Are there any? Do they work? Where do they break down?
AI readiness evaluation
Assessment of whether existing content can be reliably consumed by LLMs, RAG systems, and enterprise search tools. What would need to change to make that possible?
Gap analysis and roadmap
Prioritized findings with a clear recommendation for what to fix first, what the options are for each problem, and what a realistic transformation path looks like.
Deliverable
Gap analysis document, AI readiness scorecard, and a prioritized transformation roadmap with clear next steps.
Timeline: Typically 2–4 weeks depending on organizational complexity and content volume.