CHATBOTS

Turn Repeated Onboarding Confusion into Linear Product Issues

When the onboarding bot repeatedly fails to confidently answer questions about the same setup step, it files a deduplicated Linear issue so the product team can fix…

CategoryChatbots
Enginesim
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerOnboarding conversation escalated in IntercomIntercomIntercom
  • ActionClassify conversation into setup-step topic with OpenAIOpenAI
  • ActionIncrement per-topic escalation count in PostgresPostgreSQLPostgres
  • LogicThreshold crossed and no open issue?
  • ActionCreate friction issue in LinearLinearLinear
  • OutputStore Linear issue ID for dedupPostgreSQLPostgres

What it does

This closes the loop between support and product. Each time the onboarding concierge escalates because it could not confidently help, the topic is logged. When a single setup step generates enough low-confidence escalations, a Linear issue is opened describing the recurring friction so the root cause gets fixed.

When to use it

Use it when the same onboarding questions keep reaching humans and you want product signal, not just deflection. It converts repeated confusion into prioritized work.

How it works

  1. 1An Intercom event for an escalated onboarding conversation triggers the flow.
  2. 2OpenAI classifies the conversation into a canonical onboarding-step topic.
  3. 3The escalation is counted per topic in Postgres.
  4. 4A logic check fires only when a topic crosses the threshold and has no open ticket.
  5. 5A Linear issue is created summarizing the friction and sample quotes.
  6. 6The Linear issue ID is stored to prevent duplicate filings.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect IntercomConversations, contacts, articles.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Connect LinearIssues, projects, cycles, triage.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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