Exact matching misses roughly 30% of duplicates hiding behind typos, nicknames, and abbreviations. Get a demo of MatchLogic's fuzzy matching software and see what your current rules miss.
Records matched
Match accuracy
Time to first profile
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No black-box matching. No months of setup. Just scored match groups with full visibility into how every variation was caught.
The AI reads your data and proposes match definitions that normally take experts days to tune. The model runs offline behind your firewall, and you approve every rule.
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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 installs inside your own network. Nothing uploads and nothing leaves, so you can evaluate fuzzy matching on real production data safely.
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Score candidate pairs through the REST API, or let any LLM run cleansing and matching workflows through the MCP server without touching your records.
Everything the enterprise version offers, shown live on your demo.
Jaro-Winkler, Levenshtein, phonetic encoding, and proprietary methods.
Set match cutoffs per rule; route borderline pairs to review.
0 to 100% per field and per pair, visible everywhere.
Definitions proposed from your data in minutes, not tuned for days.
CSV, REST API, or warehouse push.
“I pulled our conquest mail list and found a man we were paying to win back. He had bought three trucks from us, and he had a service appointment that same week. We were spending real money chasing a customer who had never once left.”
Director of Marketing and Customer Retention, Ancira Auto Group
A specialist runs live pairs and shows the score each algorithm produces, then adjusts thresholds so you can watch matches appear and drop out. Bring the name pairs your current process misses.
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 is a full on-premise install, so records never leave your network. The AI that proposes rules is a custom language model installed with the product, completely offline behind your firewall, and an LLM connected through the MCP server never accesses your data.
Exact matching only finds identical values, missing duplicates with any variation. Fuzzy matching uses algorithms to score similarity, catching Jon Smith matching John Smith at 92% confidence. It finds the typos, nicknames, abbreviations, and format differences that exact matching misses.
MatchLogic combines multiple fuzzy algorithms, including Jaro-Winkler distance, Levenshtein edit distance, and phonetic encoding, with deterministic rule logic. Every match carries the rule and field-level scores that produced it, so results stay explainable and auditable.
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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