MatchLogic's entity matching software matches records to real people and companies with pre-trained AI: accurate from day one, deterministic on every run, and explainable down to the field. No model training, no black box. See it on your data.
Records matched
Match accuracy
Time to first profile
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Every entity match shows the evidence that produced it, so data teams and reviewers see the same reasoning.
When Bob Smith matches Robert Smith Jr. you see name similarity (87%), address match (100%), and phone correlation (95%). Adjust any threshold.
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MatchLogic matches records to real entities accurately out of the box, with no model training and no tuning required. Deterministic and explainable: same input, same answer.
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MatchLogic installs inside your network, air-gapped if needed. Party and KYC records stay in your environment through evaluation and production.
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Connect any LLM through the MCP server and let it drive matching workflows end to end. The model runs the workflow but never reads your records.
Everything the enterprise version offers, shown live on your demo.
One engine for individuals, organizations, and the records that mix both.
Match definitions proposed from your own data, approved by you.
Name, address, and identifier scores on every AI match.
Ask questions about match results in plain language.
CSV, REST API, or warehouse push with stable entity IDs.
The same shipper kept calling about a different invoice each week, but the system showed her as a brand-new customer every time. When we dug in, this ‘new’ customer had moved 1,200 shipments through us in 18 months, just split across four party records.
Director of Commercial Operations, JA Frate
A specialist matches live records to entities in front of you: exact hits, fuzzy variations, and sound-alikes, each with evidence and a confidence score. Bring examples of names that should match but do not, and watch how the engine scores them.
Fill out the form and pick a time that works. The demo runs about 30 minutes on your data scenario, with time for questions on your systems and compliance requirements. No commitment afterward.
No. The matching AI is pre-trained and accurate out of the box, with no training period and no labeled data. It is also deterministic and non-generative: the same input matches the same way every run, with no hallucination risk.
Entity matching finds when Mike Rogers, Michael Rogers, and M. Rogers are the same person, and when one company appears under different legal names, suffixes, and abbreviations. It resolves nicknames, typos, and format differences that split one entity into many records.
Yes - schedule profiling hourly, daily, weekly, or triggered by data loads via API. Embed profiling directly in your data pipelines to catch quality issues before they hit production. Set threshold alerts for when duplicate rates exceed limits or quality scores drop. Automated profiling keeps constant watch without manual intervention.
Yes. MatchLogic is a full on-premise install, so records never leave your network. MatchSense runs entirely inside your environment, including air-gapped, and an LLM connected through the MCP server operates workflows without ever accessing your data.
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