CRM
AI Duplicate Contact Merger for HubSpot
Scans HubSpot contacts on a schedule, uses AI to score fuzzy duplicates, then auto-merges high-confidence pairs and flags the rest in Slack for review.
How it runs
The automated pipeline, trigger to output.
- TriggerRun on a schedule (nightly/hourly)
- ActionFetch and block candidate contactsHubSpot
- LogicAI scores each duplicate pairOpenAI
- ActionAuto-merge high-confidence duplicatesHubSpot
- OutputPost ambiguous pairs + summary to review channelSlack
What it does
This workflow keeps your HubSpot contact database clean by finding and merging duplicate people automatically. On a recurring schedule it pulls recent and recently-modified contacts, groups likely matches (same email domain, similar names, shared phone numbers), and asks an OpenAI model to judge whether each candidate pair is truly the same person. Pairs above a confidence threshold are merged in HubSpot — preserving the richer record and rolling up activity, deals, and notes. Anything ambiguous is posted to a Slack channel with the reasoning so a human can make the call instead of risking a bad merge.
When to use it
Use it when inbound forms, imports, sales reps, and integrations have left your CRM full of "John Smith" / "j.smith@acme.com" / "John S. (Acme)" style duplicates. It is ideal for RevOps and marketing-ops teams who want continuous hygiene rather than a quarterly cleanup project, and who do not trust HubSpot's built-in exact-match dedupe to catch fuzzy cases. Run it nightly for steady databases or hourly during a heavy migration or list-import period.
How it works
A schedule trigger fires the run. The workflow fetches contacts from HubSpot and clusters potential duplicates using deterministic blocking keys (normalized email, name, phone) so OpenAI only ever evaluates plausible pairs — keeping token cost low and accuracy high. Each candidate pair is sent to OpenAI, which returns a match probability plus a short rationale. The workflow branches on the score: pairs at or above the auto-merge threshold are merged directly through the HubSpot API (keeping the canonical record), while lower-confidence pairs are skipped from merging and instead pushed to Slack as a review queue with deep links to both records. A final summary message reports how many were merged versus flagged so the team has an audit trail every run.
Set it up
What you configure once, before turning it on.
- 1Connect HubSpotCRM, deals, marketing, support.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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