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TICKET MANAGEMENT

Deduplicate Intercom bug reports against an existing Linear issue ledger

Before creating a new Linear issue, this checks a Postgres ledger and Linear search for an existing matching bug.

CategoryTicket Management
Enginesim
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerIntercom conversation tagged 'bug'IntercomIntercom
  • ActionGenerate symptom fingerprintOpenAI
  • ActionLook up fingerprint in Postgres ledgerPostgreSQLPostgres
  • LogicBranch: known issue vs new
  • ActionLink or create Linear issue + recordLinearLinear
  • OutputPost Linear link to IntercomIntercomIntercom

What it does

Stops the same bug from spawning ten duplicate Linear issues. When a conversation is flagged as a bug, it fingerprints the symptom, checks a Postgres ledger of known bugs and searches Linear for a match. On a hit, it attaches the new report to the existing issue and bumps its occurrence count; on a miss, it creates a new Linear issue and records its fingerprint in the ledger.

When to use it

When a single regression generates a wave of support tickets and your Linear board fills with near-identical bugs that engineers waste time closing as duplicates.

How it works

  1. 1An Intercom conversation tagged "bug" fires the trigger.
  2. 2An OpenAI step produces a normalized symptom fingerprint from the thread.
  3. 3The flow queries a Postgres known-issues ledger for a matching fingerprint.
  4. 4A logic step branches: known issue versus new issue.
  5. 5Known path adds a comment and increments the report counter on the existing Linear issue; new path creates a Linear issue and inserts its fingerprint into Postgres.
  6. 6The flow posts the resolved Linear link back to the Intercom conversation.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect IntercomConversations, contacts, articles.
  2. 2
    Connect LinearIssues, projects, cycles, triage.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Connect OpenAIModels, embeddings, files.
  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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