Consolidate duplicate records with visibility into what should merge and which values survive. MatchLogic combines transparent matching with offline AI-recommended setup so your team can review the logic before trusted records move downstream.
Records matched
Match accuracy
Time to first profile
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MatchLogic keeps matching evidence and consolidation logic visible before records are merged. AI can recommend how the data should be cleaned and matched, while your rules determine what belongs together and what moves into the trusted output.
Consolidate duplicate versions of the same entity into cleaner records that downstream teams can use with fewer conflicts and inconsistencies.
Spend less time sorting through duplicate candidates by hand before deciding which records belong together and should move forward.
Review matching evidence and consolidation decisions before trusted records are written back or sent downstream, reducing the risk of hard-to-reverse merge mistakes.
Use offline AI to reduce cleansing and matching setup work, then extend automation through MCP so an approved LLM can operate workflows without accessing the underlying records.
Profile, clean, match, review, consolidate, and export in one workflow, with visibility into how records were grouped and how the final output was assembled.
Identify quality issues and standardize values before duplicate records are grouped for consolidation.
Get suggested cleansing rules and match definitions based on your data before reviewing duplicate groups.
Inspect matching evidence before deciding which records should be consolidated.
Apply your rules for how trusted records are assembled and review the resulting output before it moves downstream.
Send consolidated records downstream through supported export and operational workflows.
Bring a representative duplicate dataset. See how MatchLogic identifies duplicate records, applies controlled survivorship rules, and produces consolidated output for review.
1,204 duplicate pairs
96.4% confidence
312 master records
18 fields comparedNow when a shopper tells us they have two accounts, the answer is usually that we already merged them the night before. The history follows the person and the offers follow the household. My team is not spending its week stitching records back together by hand.
Director of Loyalty and Customer Data, Schnucks
Yes. MatchLogic keeps matching evidence visible so duplicate groups can be reviewed before records are consolidated.
Its offline AI recommends cleansing rules and match definitions from your data. Your team reviews the setup and matching results before consolidation logic is applied.
Yes. MatchLogic supports controlled merge and survivorship workflows so the final output follows your rules rather than an unexplained automated decision.
Yes. The offline AI can run behind your firewall with no cloud calls, keeping sensitive data under your control throughout the matching workflow.
MCP can let an approved LLM operate broader cleansing and matching workflows without giving that LLM access to the underlying records.
Yes. Bring a representative sample or merge-purge scenario, and the walkthrough can focus on how records are grouped, reviewed, and consolidated for your use case.
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