What it does
Use when a user mentions "CRM record merge suggestions", "real-time dedup workflow", "duplicate detection on create", or asks for related help. Real-time on record creation: find similar existing records, AI computes match confidence, high-confidence pairs auto-merge, medium-confidence flag for review, low-confidence ignore. Prevents duplicates entering the CRM rather than cleaning them up later.
You get
Workflow
Workflow produced by this recipe.
step
Workflow Step produced by this recipe.
How it works
Build the Real-Time Dedup Workflow
Create WorkflowCreate a workflow
Find Similar Existing Records
configure_find_records_stepConfigure find-records step that searches for existing records similar to the newly-created one. Match criteria: same domain (for accounts), same email (for users), fuzzy name match (Levenshtein distance < 3), same primary contact. Returns: list of candidate matches with similarity scores.
Branch on Match Found
configure_workflow_branch_stepBranch step: did the search find any candidate matches? If NO matches — record is unique, proceed to standard onboarding flow (handoff to auto-enrich-new-accounts). If YES matches found — continue to AI confidence scoring.
AI Compute Match Confidence
configure_ai_research_stepConfigure AI step that compares the new record to each candidate match and produces a confidence score (0-100) per pair. Inputs: all available fields, recent activity, contextual clues (e.g. same source UTM suggests same person). Considers nuances (e.g.
Multi-Split by Confidence
configure_workflow_multi_split_stepMulti-split based on top candidate
High-Confidence: Auto-Merge
configure_update_attribute_stepOn high-confidence branch: merge the new record into the existing one. Keep the existing record as survivor (preserves history), copy any new fields from the new record, mark the new record as
Medium-Confidence: Flag for Review
configure_create_task_stepOn medium-confidence branch: create a RevOps task with both records side-by-side, AI
Validate and Publish
publish_workflowValidate and publish. Monitor: auto-merge volume per week (proves the workflow is catching real duplicates), false-positive rate (sample audit of auto-merges — were any wrong?), review-queue depth (high = too many medium-confidence cases, suggests AI prompt tuning). Together with the scheduled-data-quality-audit, this is the dedup defense-in-depth pattern.
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