CUSTOMER SUPPORT
Audit KB Coverage by Conversing With Support Data
An agent-driven assistant you chat with to investigate where your knowledge base falls short — it queries unresolved tickets, checks Confluence coverage.
How it runs
The automated pipeline, trigger to output.
- TriggerChat session opened
- ActionQuery unresolved/escalated ticketsZendesk
- ActionSearch existing Confluence coverageConfluence
- LogicReason about true gaps and draft on confirm
- ActionOpen Linear task per confirmed gapLinear
- OutputSummarize findings in chat
What it does
This is a conversational coverage auditor. You ask it questions like 'where are we weakest on billing questions?' and it works through your support data: pulling unresolved or escalated Zendesk tickets, checking what Confluence already covers, identifying true gaps, and drafting articles on demand. It reasons across systems rather than running a fixed pipeline.
When to use it
Use it for open-ended documentation strategy sessions, exploratory audits, or when you want to interrogate the gap landscape interactively instead of receiving a static weekly report.
How it works
- 1You open a chat session with the agent.
- 2The agent queries Zendesk for unresolved and escalated tickets matching your line of questioning.
- 3It searches Confluence to see which topics already have coverage.
- 4It reasons about where real gaps exist and proposes drafts, writing full articles when you confirm.
- 5For each confirmed gap it opens a Linear task and links any draft created, then summarizes findings back to you in the chat.
Set it up
What you configure once, before turning it on.
- 1Connect ZendeskTickets, queues, knowledge base.
- 2Connect ConfluenceSpaces, pages, blueprints.
- 3Connect OpenAIModels, embeddings, files.
- 4Connect LinearIssues, projects, cycles, triage.
- 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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