Pick your engine. Scale when you're ready.
One rule of thumb: MatchCore grows with your team. MatchSense grows with your data.
Unlimited records, containerized on any server or cloud, multi-user with SSO.
See this plan →How should match decisions
get made?
Records in a typical matching
project?
Where does it
need to run?
RECORDS UNDER MANAGEMENT
EVERY TIER INCLUDES
Camber
Sarah Chen, VP of Engineering
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.
1,400
Duplicate records
stopped
1 patient
One complete
history
Midwest Community Bank
Anne Hartzell, Vice President of Operations
Finding 850 high-value households we had never properly identified was humbling. These were people who had been banking with us for ten or twenty years, and we had been treating them as four different small relationships instead of one big one.
850
Relationships
uncovered
10+ years
Of overlooked
value
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 - 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.
Profiling automatically flags PII in wrong fields, incomplete required data, and audit risks. It identifies records missing mandatory fields, catches format violations, and documents quality scores for compliance reporting. Every profile run creates audit trails showing your data quality status and improvement trends - turning audit panic into audit proof.
Every demo starts with your data. Bring a sample file and we will walk through profiling, cleansing, matching, and golden record assembly live. You will see how the platform handles your specific data quality challenges. No slide decks. No hypothetical scenarios.
Schedule a Demo
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