Our Technology
Stop configuring match rules. Start teaching your match service.
Machine learning trained on your data. Designed to resolve the matches legacy platforms leave behind.
Why traditional match platforms fall short
Every organization defines a match differently — and traditional platforms make you spell out those rules in painstaking detail. The result is brittle configurations that rarely reflect how your team actually decides what counts as a duplicate.
We do it the other way around. Our machine learning service learns your matching logic from sample data, then applies it consistently across millions of records. It's like adding a data steward who never tires, never deviates, and scales with your volume.
Our Technology
Why machine learning beats rules-based matching
Why Machine Learning Is a Better Technology
Over 17 years of building match solutions, we've worked extensively with all three primary approaches — deterministic, probabilistic, and machine learning. Our experience has been conclusive: machine learning consistently outperforms the alternatives. Here's why.
Trained Once, Predictable Always
The model learns your matching logic from sample data during training. Once validated, results are deterministic — the same record pair always produces the same answer. No drift, no surprises, no untraceable AI decisions.
Scales Without Compromise
Match millions of records without sacrificing speed or accuracy. ML algorithms maintain performance at scale where rules-based systems slow down, require sharding, or break under volume
Higher Match Accuracy
Machine learning consistently outperforms rules-based and probabilistic systems on real-world match quality — particularly on the ambiguous edge cases where traditional approaches force a human to step in.
Captures Subtle Patterns
Typos, transpositions, partial data, cultural name variations — the patterns experienced data stewards recognize intuitively. Rules engines force these into rigid logic; machine learning captures them the way your best people do.
Fewer Errors, Both Kinds
Traditional matching forces a tradeoff: aggressive rules create false positives (records linked that shouldn't be), conservative rules create false negatives (duplicates left behind). Machine learning reduces both — the records you shouldn't have linked, and the ones you should have.
Machine learning isn't a different flavor of automation — it's a fundamentally different approach. Where traditional platforms require ongoing rule configuration to keep up with your organization, an ML model trained on your data already knows how you match.
Frequently Asked Questions
Can I trust machine learning for record matching?
Absolutely. Just as you trust your current match platform's rules, you can trust a machine learning model — once it's been trained on your data and validated against your team's expectations. The difference is that ML captures the nuances of how your team actually matches, rather than forcing those judgments into rigid configuration files.
Does your approach work with our existing MPI or EMPI platform?
​Yes. You have two ways to deploy our service. As a standalone match solution, it handles record matching entirely. As a layer on top of your existing platform — IBM, Informatica, Epic, Cerner, InterSystems, and others — it automatically resolves the potential duplicate tasks those systems leave behind, eliminating the manual review backlog.
Is this approach reliable and reproducible?
Yes. Once trained and tested, results are 100% consistent. The same record pair always produces the same match decision. There's no drift, no untraceable AI behavior, and no surprises — which is what you need for clinical, regulatory, and audit-ready environments.
How is this different from the match technology we already have?
Most enterprise match platforms use deterministic or probabilistic approaches that require configuring match rules by hand. These platforms typically leave thousands of "potential matches" — ambiguous cases that humans have to resolve manually. Machine learning captures the patterns those rules miss, and resolves the ambiguous cases that traditional platforms can't.
Will your approach meet our specific match requirements?
Yes. Our approach is customized to how your organization defines a match. Where one-size-fits-all platforms apply generic logic to every customer, we train the model on your data and your team's matching decisions — producing a service tuned specifically to your requirements.
What's the proof that machine learning is more accurate?
Across 17 years of building match solutions with all three approaches — deterministic, probabilistic, and machine learning — we've seen ML consistently outperform the alternatives. Where the difference shows up most is on the ambiguous edge cases: the records traditional systems flag as "potential duplicates" and leave for humans to resolve.