Every price.
On this page.
Pick your engine. Scale when you're ready.
MatchCore
Deterministic & Explainable – per user
MatchSense
AI Entity Resolution – per record
Pick your engine. Scale when you're ready.
One rule of thumb: MatchCore grows with your team. MatchSense grows with your data.
How should match decisions
get made?
Records in a typical matching
project?
Where does it
need to run?
"MatchCore Standard handles our day-to-day matching, and MatchCore Enterprise deploys as a container on our own infrastructure when we need to scale."
vs. desktop tools
Entry-level data matching tools cap you at 100K–250K records per import.
1M
records in a single run
vs. desktop tools
4–10×
higher import capacity than entry-level tools
vs. enterprise MDM
Enterprise MDM platforms routinely run into six figures annually and take 3–6 months to implement.
Days
to deploy
MatchLogic gives you both. MatchCore matches records with fuzzy algorithms and deterministic logic you can read and tune, while MatchSense applies pre-trained AI entity resolution that groups records on its own. Both run on your own infrastructure, and both keep every decision explainable.
Profiling instantly shows exact duplicate counts, missing data percentages, format variations, and quality scores for every field. You'll see where "McDonald's" has 20 different spellings, which systems create the most duplicates, and which fields are 40% empty. Visual heat maps highlight problem zones across all your systems simultaneously.
MatchLogic profiles 10 million records in under 8 minutes, maintaining the same speed whether scanning thousands or billions of records. The engine analyzes every field, identifies patterns, calculates quality scores, and generates visual reports without performance degradation.
Most companies discover 25-35% duplicate records they never knew existed. The average first-time profile uncovers thousands of hidden duplicates costing real money. Profiling reveals duplicates hiding behind misspellings, abbreviations, and format differences your team would never catch manually.
Yes. Profiling gives you potential duplicate percentages and risk scores before you write any matching rules. You'll know exactly how many likely duplicates exist, where they cluster, and which fields have the variations causing problems. This helps you configure match rules based on your actual data patterns, not guesswork.
Yes. You can 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.
No. The AI entity resolution engine ships pre-trained on global name, nickname, and address libraries, so it is accurate from the first run. There is no labeled dataset to assemble and no model for your team to train.
Start a free trial, or send us a sample dataset and we'll run the match and walk you through the results.
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