Government & public sector
Link citizen, benefits, and vendor records across agencies that were never designed to join.
Government data lives in agencies that grew up separately and were never meant to share a key. A resident is a taxpayer to one agency and a benefits recipient to another, and neither system was built to recognize that they are the same person. Decide benefits eligibility or catch cross-program fraud and you need those records linked, and the systems holding them have nothing exact to connect on.
Why agency systems have nothing to join on
The gap is structural, not a data-quality slip you can clean away. A tax system and a procurement database were built decades apart for different purposes, so names are entered in different order and a vendor is a “doing business as” name in one place and a registered legal name in another. No exact join survives that, and one fixed cutoff cannot be right for all of it. The cost shows up in public reports. Eligibility decided on fragmented records pays the same person twice or wrongly denies someone, and contractor spend cannot be aggregated for oversight.
Linking residents, vendors, and contractors
The link has to be learned rather than looked up in an ID that does not exist, which is what a trained matcher does. Blocking narrows the candidate pairs, and a matcher learns from about 600 labeled pairs out of your own records which agreements mean one person and which do not, with active learning keeping the labeling light. You train a separate model for residents and another for vendors, since each is keyed and written differently.
Keeping resident data inside your boundary
MadMatcher runs on infrastructure you control, so resident data stays inside the residency and access controls you are accountable to, with nothing copied to an outside service. As programs and records grow, the same trained matcher re-runs at scale without anyone rebuilding the rules.
How matching works → · Why a trainable matcher → · Talk to us →
Frequently asked questions
How do you link citizen records across agencies with no shared ID?
A trained matcher learns the link from the fields that do exist, such as name and date of birth, because no shared key was ever designed across a tax system and a benefits system. It decides when records that share no identifier still describe one resident.
Can it handle "doing business as" names and registered legal names for vendors?
Yes. A vendor model learns when a "doing business as" name in one database and a registered legal name in another are one company, so contractor spend can be aggregated for oversight instead of counting a single vendor as many.
Does resident data stay within our residency and access controls?
It runs on infrastructure you control, so resident data stays inside the residency and access controls you are accountable to, with nothing copied to an outside service. The matching happens where the data already lives.
Have a matching problem?
Book a call to scope it with the team, or explore the code on GitHub.