CUSTOMER SUPPORT

Cluster the week's Zendesk tickets into KB article proposals

On a weekly schedule, groups the past week's resolved Zendesk tickets by recurring theme and proposes one Notion article draft per cluster.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule (Monday)
  • ActionPull last 7 days of resolved ticketsZendeskZendesk
  • ActionCluster tickets into themesOpenAI
  • LogicKeep clusters above volume threshold
  • ActionDraft one article per clusterOpenAI
  • OutputWrite proposals to Notion databaseNotionNotion

What it does

Instead of one article per ticket, this workflow looks at a whole week of resolved tickets, finds the topics that came up most often, and proposes a single consolidated draft for each recurring theme. Editors see exactly which undocumented issues are driving the most support volume.

When to use it

Run it when ticket-by-ticket drafting would bury your editors in near-duplicates. Ideal for teams that want to prioritize documentation by impact, writing the one article that deflects fifty tickets rather than fifty articles nobody reads.

How it works

  1. 1A weekly schedule kicks off the run every Monday morning.
  2. 2The flow pulls all tickets resolved in the trailing seven days from Zendesk with their subjects and resolutions.
  3. 3OpenAI clusters the tickets into themes and ranks each cluster by volume and effort saved.
  4. 4A logic step keeps only clusters above a minimum ticket count, dropping one-off issues.
  5. 5For each surviving cluster, OpenAI drafts a consolidated article covering the shared root cause and fix.
  6. 6Each draft is written to a Notion "KB Proposals" database with its ticket count and example links for the editor to approve.

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 NotionPages, databases, comments.
  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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