Records describing the same person rarely share an identifier across systems. Get a demo of MatchLogic's record linkage software and watch records link with scored, explainable evidence.
Records matched
Match accuracy
Time to first profile
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No black-box matching. No months of setup. Just linked records with full visibility into why every pair was joined.
Point MatchLogic at two sources and see which records describe the same person the same morning. No consultants required.
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When Bob Smith links to Robert Smith Jr. you see name similarity (87%), address match (100%), and date of birth agreement. Adjust any threshold.
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The AI proposes cleansing rules and linkage definitions from your own data, then the deterministic engine executes them. The model runs offline behind your firewall.
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Trigger linkage runs through the REST API, or connect any LLM through the MCP server to run workflows end to end. The model never accesses patient or citizen records.
Everything the enterprise version offers, for 14 days.
Link records across registries, clinical systems, and archives without shared keys.
Borderline pairs route to human review before anything joins.
The rule and confidence score behind every pair, logged.
CSV, REST API, or warehouse push.
Cleansing and linkage rules proposed from your data, approved by you.
“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
A specialist links two sources with no shared identifier in front of you, showing how name, date of birth, and address evidence combine into a confidence score, and how borderline pairs route to review. Bring a de-identified sample if you prefer.
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.
Yes. MatchLogic installs fully on-premise, so sensitive records such as patient or citizen data never leave your network. The AI recommendations run on a custom language model installed with the product, completely offline, and an LLM connected through the MCP server never accesses the records.
MatchLogic compares records across multiple fields such as names, dates of birth, addresses, and contact details, scoring similarity with fuzzy and phonetic algorithms. Records link when combined field evidence crosses your configured threshold, and every link carries the rule that produced it.
Confidence tiers protect against false links. High-confidence pairs can link automatically while borderline candidates route to a review queue, and every decision is logged with full before and after history. Thresholds are tuned to your risk tolerance.
The Docker-based install runs on any infrastructure in under 5 minutes, entirely inside your own network. You can connect SQL Server, PostgreSQL, MySQL, Oracle, or flat files, or load a CSV directly.
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