SUMMARIZATION

Extract decisions from resolved Front threads into a Notion registry

When a Front conversation is archived, an AI reads the full thread, pulls out every decision that was made.

CategorySummarization
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFront conversation archived/resolvedFront
  • ActionFetch full conversation message historyFront
  • ActionExtract discrete decisions with owner + rationaleOpenAI
  • LogicSkip if no decision detected
  • OutputWrite each decision as a row in Notion registryNotionNotion

What it does

Turns the back-and-forth of an email thread into a clean, searchable record of decisions. When a Front conversation is resolved or archived, this workflow reads the entire message history, identifies the concrete decisions reached (what was decided, who decided it, and the rationale), and writes each one as a row in a Notion database that the whole team can search later.

When to use it

Use it when important calls get buried in customer or vendor email threads and nobody can find "what did we actually agree to" three weeks later. Ideal for ops, partnerships, and account teams who negotiate over Front and need an auditable decision trail without manual note-taking.

How it works

  1. 1A Front conversation moves to archived/resolved, firing the trigger.
  2. 2The workflow pulls the full ordered message history for that conversation.
  3. 3An OpenAI model extracts a list of discrete decisions, each with a one-line summary, owner, date, and supporting quote.
  4. 4A filter drops the thread if no decision was detected, avoiding noise.
  5. 5Each decision is written as a structured page in the Notion decisions registry, linked back to the source Front conversation.

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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