AI & RAG
Find documentation gaps from tickets the knowledge base can't answer
Weekly, scans recently resolved Zendesk tickets, tests each against the knowledge base.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly gap-analysis run
- ActionPull recently resolved tickets from ZendeskZendesk
- LogicKeep tickets with low knowledge-base retrieval scoresSupabase
- ActionCluster gaps and propose article outlines with OpenAIOpenAI
- OutputPublish ranked doc-gap report to ConfluenceConfluence
What it does
Turns support history into a documentation backlog. It looks back over recently resolved tickets, checks which ones the knowledge base could not have answered confidently, clusters those misses by topic, and delivers a ranked list of missing or weak articles for the docs team to fix.
When to use it
For knowledge and content teams who want to prioritize doc work by real customer demand instead of guesswork. A perfect monthly or weekly companion to any RAG answering workflow — it tells you why deflection is leaking.
How it works
- 1A weekly schedule fires the trigger.
- 2Recently resolved tickets are pulled from Zendesk.
- 3Each ticket question is run against the Supabase index; low-retrieval-score tickets are kept as likely doc gaps.
- 4OpenAI clusters the gap tickets into themes and proposes article titles and outlines.
- 5The ranked gap report is posted to a Confluence page for the docs team to action.
Set it up
What you configure once, before turning it on.
- 1Connect ZendeskTickets, queues, knowledge base.
- 2Connect SupabaseTables, auth, storage, edge functions.
- 3Connect OpenAIModels, embeddings, files.
- 4Connect ConfluenceSpaces, pages, blueprints.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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