TICKET MANAGEMENT

Weekly audit of duplicate-merge accuracy from Front

On a weekly schedule, it reviews merges performed in Front against customer reopens, scores how often merges were correct.

CategoryTicket Management
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule
  • ActionFetch merged duplicates + reopen activityFront
  • LogicLabel each merge good or suspect
  • ActionJoin outcomes with similarity scoresPostgreSQLPostgres
  • ActionSummarize patterns + recommend thresholdOpenAI
  • OutputPublish accuracy report to NotionNotionNotion

What it does

This workflow keeps your auto-merge logic honest. Each week it pulls the conversations that were merged as duplicates, checks whether any were reopened or disputed by the customer, computes a merge-accuracy score, and publishes a Notion report highlighting where merges went wrong.

When to use it

Run it once duplicate merging is live and you need ongoing evidence that it isn't burying distinct customer issues. The report tells you whether to loosen or tighten your similarity threshold.

How it works

  1. 1A weekly scheduled trigger starts the run.
  2. 2The workflow fetches conversations merged as duplicates in the past week from Front, along with their reopen and reply activity.
  3. 3A logic step labels each merge as confirmed-good (no reopen) or suspect (reopened or customer pushback).
  4. 4A Postgres query joins these outcomes with the original similarity scores to find the threshold where accuracy degrades.
  5. 5OpenAI summarizes the patterns and recommends a threshold adjustment.
  6. 6The workflow writes a dated accuracy report to Notion with the score, suspect-merge list, and the recommendation.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FrontShared inbox, conversations.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect NotionPages, databases, comments.
  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.

Run this workflow in your colony.

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