CRM

Score Intercom conversation sentiment and decay Attio warmth on each reply

On every closed Intercom conversation, an AI classifier reads the full thread and produces a relationship-strength delta that updates the Attio account's warmth score.

CategoryCRM
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerIntercom conversation closedIntercomIntercom
  • ActionFetch full conversation transcriptIntercomIntercom
  • ActionClassify sentiment and emit warmth deltaOpenAI
  • ActionResolve contact to Attio companyAttio
  • LogicApply time decay then add delta
  • OutputWrite warmth and rationale to AttioAttio

What it does

Reads each closed Intercom conversation in full and uses an LLM to judge the relationship signal, frustration, satisfaction, buying intent, then converts that judgment into a warmth delta applied to the matched Attio company. It complements manual tags by catching sentiment the team never labeled.

When to use it

Use it when conversation tone matters more than which canned tag a rep happened to pick, or when tagging discipline is inconsistent. Good for teams that want warmth scores to reflect how customers actually feel across many small interactions.

How it works

  1. 1An Intercom conversation closes, triggering the workflow.
  2. 2The full message transcript is pulled from the conversation.
  3. 3An OpenAI step classifies the thread into a sentiment band and returns a signed delta with a one-line rationale.
  4. 4The contact is resolved to an Attio company record.
  5. 5A logic step applies time decay to the existing warmth before adding the new delta, so recent interactions weigh more.
  6. 6The recomputed warmth and the AI rationale are written back to the Attio company.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect IntercomConversations, contacts, articles.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect AttioReal-time CRM with structured data + powerful views.
  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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