Knowledge Base AI Migration Audit (Onboarding)

Knowledge Base AI Migration Audit (Onboarding)

1. 01

   Inventory all existing knowledge sources across the client's organization

   Catalog support tickets, internal wikis, product documentation, and any legacy help center content. Note the format, ownership, and update frequency of each source.

2. 02

   Assess the AI readiness of each source for indexing

   Check for unstructured formats, missing metadata, or outdated information that could degrade AI answer accuracy. Flag sources that require cleanup before migration.

3. 03

   Define the scope of the self-service funnel with the client

   Agree on which customer segments and query types the AI assistant should handle first, and set a baseline for ticket deflection rate to measure success.

4. 04

   Evaluate vendor lock-in risks before committing to a platform

   Review each candidate's API openness, data export capabilities, and migration paths. For example, HelpCenter.io offers white-labeling but check if you can move content out easily if the client switches.

5. 05

   Run a pilot index with a subset of high-value content

   Select 50-100 articles covering the most common support queries and test the assistant's response quality. Compare accuracy across tools like Kapa, which pulls from 30+ sources, versus simpler platforms.

6. 06

   Establish a content governance workflow for ongoing updates

   Define who owns content refreshes, how often the index is re-synced, and how new articles get added. This prevents drift between the knowledge base and the AI's answers.

7. 07

   Document the migration plan and rollback criteria

   Write down the step-by-step cutover, including data mapping, timeline, and a rollback trigger if ticket volume spikes or answer accuracy drops below the agreed threshold.