CRM

Cross-CRM Company Dedupe with Human Merge Queue

Scans Salesforce and HubSpot on a schedule, fuzzy-matches company records across both systems.

CategoryCRM
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule fires
  • ActionFetch updated companies from SalesforceSalesforce
  • ActionFetch updated companies from HubSpotHubSpotHubSpot
  • LogicFuzzy-match pairs and drop already-resolved
  • ActionInsert candidate pairs into Postgres merge queuePostgreSQLPostgres
  • OutputPost Slack approval card per pairSlack

What it does

Pulls company records from both Salesforce and HubSpot, scores candidate duplicate pairs using normalized name and domain matching, and writes each high-confidence pair into a Postgres merge queue. A reviewer gets a Slack card with both records side by side and Approve / Reject buttons — nothing is merged automatically.

When to use it

You run two CRMs (sales on Salesforce, marketing on HubSpot) and the same company exists in both under slightly different names. You want a steady, auditable stream of merge proposals instead of a risky one-time bulk dedupe.

How it works

  1. 1A nightly schedule fires the run.
  2. 2Fetch companies updated since the last watermark from Salesforce and HubSpot.
  3. 3Normalize names and domains, then score every cross-system pair; keep pairs above the match threshold.
  4. 4A logic step drops pairs already resolved or already queued.
  5. 5Insert surviving candidates into the Postgres `merge_queue` table with a status of pending.
  6. 6Post a Slack approval card per pair showing both records and their match score.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SalesforceAccounts, opportunities, cases.
  2. 2
    Connect HubSpotCRM, deals, marketing, support.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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