CUSTOMER SUPPORT

Macro Coverage Gap Detector on Ticket Solve

When a ticket is solved without any macro applied, checks whether its answer matches an existing macro and, if not, logs it to Postgres as an uncovered answer so recurring gaps…

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerTicket solved in ZendeskZendeskZendesk
  • LogicKeep only tickets solved without a macro
  • ActionEmbed reply and match against macro libraryOpenAI
  • LogicBranch on whether a close macro exists
  • ActionUpsert uncovered answer into gaps tablePostgreSQLPostgres
  • OutputPost gap leaderboard to SlackSlack

What it does

Catches the moments your macro library missed. The instant an agent solves a ticket by typing a reply from scratch, it compares that reply against your existing macros; if nothing close exists, it records the gap. Over time the Postgres table shows which hand-written answers recur often enough to deserve their own macro.

When to use it

When you already have a macro library but suspect it has blind spots, and you want a continuously-updated, data-backed list of what to add next rather than a once-a-quarter manual audit.

How it works

  1. 1A Zendesk ticket-solved event triggers the run.
  2. 2Filter to only tickets solved without a macro applied.
  3. 3Use OpenAI to embed the agent's reply and compare it to your existing macro library for a close match.
  4. 4Branch: if a strong match exists, stop — coverage is fine.
  5. 5If no match, upsert the reply's intent into a Postgres gaps table, incrementing a count for that recurring answer.
  6. 6Output the running gap leaderboard to a Slack channel so leads see the top uncovered answers worth turning into macros.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ZendeskTickets, queues, knowledge base.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  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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