CUSTOMER SUPPORT

Detect bug reports in Intercom and open a Linear issue

Reads new Intercom conversations, uses an LLM to decide if it's a real bug, and for confirmed bugs creates a triaged Linear issue and replies to the customer with the tracking…

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Intercom conversationIntercomIntercom
  • ActionClassify + extract bug details with OpenAIOpenAI
  • LogicConfirmed bug report?
  • ActionCreate labeled Linear issueLinearLinear
  • OutputAdd issue link as internal note in IntercomIntercomIntercom

What it does

This watches inbound Intercom conversations and uses an LLM to separate genuine bug reports from how-to questions, feature requests, and billing chatter. When it identifies a bug, it drafts a clean reproduction summary, opens a Linear issue with a severity label, and posts an internal note back on the Intercom conversation linking the two so support can track resolution.

When to use it

Use it when bug reports get lost in a shared inbox and engineering only hears about them days later. It turns customer language into a structured, labeled engineering ticket the moment the report lands.

How it works

  1. 1A new Intercom conversation triggers the flow.
  2. 2OpenAI classifies the message and, if it's a bug, extracts a title, repro steps, and severity.
  3. 3A branch routes only confirmed bugs forward; everything else exits.
  4. 4A Linear issue is created with the title, body, and a severity label.
  5. 5An internal note with the Linear issue link is added back to the Intercom conversation for the support agent.

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 LinearIssues, projects, cycles, triage.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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