Raise match rates without rebuilding your graph.
Identity and data companies use DarkMatch as a semantic layer on top of their own graph to recover connections their rules miss, and prove the lift with a standard benchmark.
What breaks today
Match rates plateau
Rules catch the easy links. The messy ones, nicknames, moves and typos, stay unresolved.
Mega-clusters erode trust
Permissive rules build households of hundreds of people, and every downstream count inherits the error.
Buyers want proof
Customers ask how your graph compares, and a match rate alone doesn’t answer it.
How DarkMath helps
Three products, used in the order your question needs them.
| Product | What it does for you |
|---|---|
| DarkMatch | Layers on top of your graph and recovers links rules miss, with confidence and sources. |
| DarkAgent | Runs coverage and overlap analysis and prepares standard reports for your customers. |
| DarkMirror | Adds semantic attributes and lookalike audiences to your product line. |
Where the lift comes from
In the June 2026 benchmark, rules-based matching alone found 55% of real connections. Adding the DarkMath semantic layer raised that to 89% on the same records. Household precision matters as much as reach: in an earlier test, the largest household DarkMath built had 37 members, against 994 for the incumbent.
Questions teams bring us
- Entity resolution tools that work on top of an existing identity graph
- How do data providers raise match rates for their customers?
- How should we measure false duplicates when comparing identity vendors?
Data providers questions
Do you compete with our graph?
DarkMatch adds a layer to your graph. Your graph stays yours, and partnership models are available.
Can we compare coverage both ways?
Yes. A net-new analysis shows the identifiers each side can add to the other, signal by signal.
Start with your own records.
Send a sample and get a standard report: duplicates, households, match rate and what we can add.