Why teams choose MadMatcher.
We compare on approach rather than on logos. Four things MadMatcher does differently.
Trainable to your domain
MatchFlow learns a matcher from your labeled pairs, rather than applying one fixed pretrained model. Accuracy improves as you add labels.
Runs in your infrastructure
Everything runs in your own Spark or single-machine environment. Your data does not leave your perimeter for a vendor cloud.
Blocking that is benchmarked
Sparkly’s TF/IDF blocking was published at VLDB 2023 and outperforms eight state-of-the-art blocking solutions. The method and benchmarks are public.
Composable by design
Use one tool or all three. MadMatcher slots into your existing pipeline instead of making you adopt a full platform.
Backed by published research.
The blocking core was introduced and benchmarked in a peer-reviewed VLDB 2023 paper, and builds on a decade of work from UW–Madison’s Magellan group. You can read the method and the numbers yourself.
Have a matching problem?
Book a call to scope it with the team, or explore the code on GitHub.