Resolve records into trusted people and organizations without training a customer-specific model. MatchLogic uses pre-trained, deterministic entity-resolution AI that runs inside your environment and produces explainable results.
Records matched
Match accuracy
Time to first profile
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MatchLogic resolves identity using pre-trained, non-generative AI. The same input produces the same result, and the outcome remains explainable, so your team gets AI-driven entity resolution without handing identity decisions to an opaque model.
Connect records spread across systems into a clearer view of the same person or organization, even when attributes are inconsistent or incomplete.
Spend less time tracing duplicate identities, conflicting attributes, and fragmented histories across separate systems before the data can be used.
Give data owners, governance teams, and other reviewers explainable resolution outcomes instead of asking them to trust an opaque model decision.
Use MCP for broader cleansing and matching workflows so an approved LLM can operate the process without receiving access to the underlying records.
Bring records together, resolve identity across inconsistent attributes, review the outcome, and send trusted entity results downstream while keeping sensitive data inside your environment.
Resolve identity without customer model training, tuning, or a separate data-science project.
Connect records that refer to the same real-world person or organization across inconsistent source data.
Get repeatable results from identical input rather than generative or probabilistic behavior.
Review the basis of resolution outcomes so technical and governance teams can understand what happened.
Keep entity-resolution processing inside infrastructure you control, including isolated environments.
Bring a representative entity dataset. See how MatchLogic connects records that belong to the same person or organization and review the evidence supporting each resolved entity.
1,204 duplicate pairs
96.4% confidence
312 master records
18 fields comparedThe first time we ran it against a year of claims, it drew a map we had never been able to see. One address tied together eleven claims we had treated as strangers. We did not have to take the system's word for it, because it showed the reason behind every line it drew.
Director of the Special Investigations Unit, Acuity Insurance
No. The entity-resolution AI is pre-trained and does not require customer model training or tuning before use.
No. It is non-generative and deterministic, so identical input produces identical results.
Yes. MatchLogic provides explainable entity-resolution outcomes rather than an opaque black-box verdict.
Yes. MatchLogic provides explainable entity-resolution outcomes rather than an opaque black-box verdict.
Yes. MatchLogic can run entity resolution on-premise, including air-gapped environments where data and processing must remain isolated.
MCP is a separate MatchLogic layer for broader cleansing and matching workflows. An approved LLM can operate those workflows without receiving access to the underlying records.
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