CRM

Agent-driven Intercom topic enrichment across the whole HubSpot book

A Paperclip agent works through every HubSpot contact lacking topic context, gathers each one's Intercom history, reasons about the recurring themes.

CategoryCRM
Enginepaperclip
Difficultyadvanced
Triggermanual
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerOperator starts the run for a target segment
  • ActionFetch HubSpot contacts lacking topic contextHubSpotHubSpot
  • ActionRetrieve each contact's Intercom historyIntercomIntercom
  • LogicAgent decides if signal is sufficient
  • ActionSynthesize narrative and tags with OpenAI reasoningOpenAI
  • OutputWrite summary and structured tags to HubSpotHubSpotHubSpot

What it does

Where the deterministic backfill applies one label per contact, this agent produces richer per-contact context. It iterates the full HubSpot book, and for each under-enriched contact it pulls Intercom history, reasons about what the relationship is really about across multiple conversations, and writes both a short narrative summary and a normalized set of topic tags. It self-corrects when conversations are sparse or contradictory.

When to use it

Choose this for a high-value backfill where a flat single-topic label is too thin, such as enriching strategic accounts ahead of QBRs, or when topics evolve over a long relationship.

How it works

An operator starts the run manually with a target segment. The agent fetches the HubSpot contact list, then loops: for each contact it retrieves Intercom conversations, decides whether there is enough signal, and uses OpenAI reasoning to synthesize a narrative plus tags. A logic step lets the agent skip or defer contacts with insufficient history. The agent writes the summary and structured tags back to the HubSpot contact, then continues until the segment is exhausted.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HubSpotCRM, deals, marketing, support.
  2. 2
    Connect IntercomConversations, contacts, articles.
  3. 3
    Connect OpenAIModels, embeddings, files.
  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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