CUSTOMER SUPPORT

Detect Front conversations with no matching doc and draft the missing article

When a Front conversation is resolved, checks whether the customer's question maps to an existing knowledge base article and, if none exists, drafts a new Notion article seeded…

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFront conversation resolvedFront
  • ActionExtract canonical question + resolution with OpenAIOpenAI
  • ActionSearch Notion knowledge base for a matchNotionNotion
  • LogicBranch: relevance score below threshold?
  • ActionDraft new article from resolved reply with OpenAIOpenAI
  • OutputCreate draft in Notion 'Needs review' databaseNotionNotion

What it does

Closes the gap between answered tickets and documented knowledge. Every time a support conversation is resolved in Front, the workflow extracts the underlying question, searches your Notion knowledge base for a matching article, and when nothing relevant exists it drafts a brand-new article using the agent's winning reply as the source of truth.

When to use it

Run this when your team keeps answering the same questions in tickets but the help center never catches up. It turns resolved conversations into a steady pipeline of draft articles instead of relying on agents to remember to write docs.

How it works

  1. 1A Front conversation is moved to resolved, triggering the flow.
  2. 2OpenAI distills the conversation into a single canonical question and the resolution summary.
  3. 3The workflow searches Notion for an article that already answers that question.
  4. 4A branch checks whether the top match clears a relevance threshold.
  5. 5If no match qualifies, OpenAI drafts a structured article (title, problem, steps, answer) from the resolved reply.
  6. 6The draft is created in a Notion "Needs review" database for an editor to approve.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FrontShared inbox, conversations.
  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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