Link records across systems even when identifiers are missing, inconsistent, or stored differently. MatchLogic uses offline AI to recommend cleansing and linkage rules while every link stays explainable and under your control.
Records matched
Match accuracy
Time to first profile
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MatchLogic analyzes your source data locally and recommends how records should be cleaned and compared. You review the logic before transparent matching rules are applied, so cross-system links stay inspectable instead of becoming black-box decisions.
Connect records that belong to the same person, customer, supplier, or account even when each system stores them differently. Give downstream teams a more complete view of the entity they are working with.
Recover links that exact identifiers cannot make because IDs are missing, outdated, or inconsistent across sources. Reduce the records that remain disconnected simply because one field does not line up.
Give reviewers field-level evidence behind each link so they can investigate uncertain results without reconstructing the matching logic from scratch.
Use offline AI to reduce setup work, then extend automation through MCP so an approved LLM can operate cleansing and matching workflows without accessing the underlying records.
Profile, clean, map, link, review, and export in one workflow, with the linkage logic and evidence visible throughout.
Connect records across files, databases, and systems without requiring a shared unique identifier.
Get suggested cleansing rules and linkage definitions based on the structure and content of your data.
Combine exact, fuzzy, phonetic, and configurable comparisons based on the fields that matter.
Review field-level scores and evidence behind each link, with uncertain results available for closer inspection.
Send linked results downstream through export or API for operational and recurring workflows.
Bring records from two or more sources. See how MatchLogic connects records when identifiers are missing, inconsistent, or unreliable, and review the evidence behind each link.
1,204 duplicate pairs
96.4% confidence
312 master records
18 fields compared“The difference was immediate. In the first month, we stopped creating about 1,400 duplicate patient records that our old system would have missed. That's 1,400 kids whose medical histories stayed intact instead of getting split across two profiles.”
VP of Engineering, Camber
Yes. MatchLogic can compare records across separate systems using the fields available in each source, even when a common unique ID is missing or inconsistent.
Its offline AI analyzes your data and recommends cleansing rules and linkage definitions. You review those recommendations before the matching logic is applied.
No. AI helps configure the process, while transparent deterministic and fuzzy matching logic produces the linkage results you review.
Yes. The offline AI can run behind your firewall with no cloud calls, keeping sensitive linkage data under your control.
Yes. An approved LLM can operate cleansing and matching workflows through MCP without being given access to the underlying records.
Yes. Bring a representative sample or your linkage scenario, and the walkthrough can focus on the systems, fields, and inconsistencies you actually work with.
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