DarkMatch
Match people, not records.
DarkMatch finds the same customer across nicknames, typos, moves, married names and missing fields, and keeps apart the people who only look alike. It layers on top of your current matching and recovers the connections rules leave behind.
Easy to test. Hard to argue with.
June 2026 benchmark
1,000,000 records, 250,000 real people, identical input to both systems.
Precision when matching: 98% vs 96%. Methodology
DarkMatch is DarkMath’s identity resolution product. It combines rules-based matching, probabilistic scoring and semantic matching to decide whether records describe the same person or household, then returns resolved IDs with confidence scores, sources and an audit trail of every merge.
Same name, same street, two different people.
Rules fail in two directions: they miss records that belong together, and they merge people who don’t. Step through what each method does with five ordinary records.
Three stages, each catching what the last one missed
DarkMatch runs as a cascade. Cheap, certain decisions happen first. Expensive judgment is saved for the records that need it.
In the June benchmark, rules-based matching alone found 55% of real connections; adding the semantic layer raised that to 89%, on the same records.
Augmented rules
Rules-based matching with learned weights and confidence thresholds instead of a blunt pass or fail.
Probabilistic matching
The Fellegi-Sunter model, fit with expectation maximization, estimates match probability where no shared key exists.
Semantic matching
Records become vector embeddings, so variants with the same meaning converge. Language-model review is applied only to the hardest boundary cases, where uncertainty is highest.
What DarkMatch returns
One Golden Record per identity: a single, auditable profile with every merge explained.
Clean records
Duplicates removed, names and addresses standardized, bad records flagged.
People and households
Individual and household IDs that stay stable as your data changes.
Fresher contact data
Address and contact updates from licensed reference data, where permitted.
Confidence and sources
A score, the contributing sources and the reason for every link.
Embeddings
Vector representations that make your data AI-ready: for search, models, DarkMirror audiences and DarkAgent.
Net-new coverage
A report of the identifiers we can add to your file, signal by signal.
Easy to test. Hard to argue with.
The benchmark is ours. The test is yours. Start with an example set, such as a million records you upload or we create, where the ground truth is known to both parties. Most match tests run in about two weeks, and the report is yours to keep.
The Corruptor, DarkMatch’s corruption process, then randomly and anonymously introduces realistic errors such as typos, transposed digits, nicknames, format changes, missing fields and moves. Because both sides know the right answers, you can see objectively where our semantic layer is additive: fewer duplicates, and connections that would otherwise be missed. Run your current vendor on the same file and you have an A/B test.
Start with a known set
Upload about a million records or let us create them. Hashed, raw or vector-only. Both parties know the ground truth.
Corrupt at random
The Corruptor alters copies randomly and anonymously. Your originals never change.
A/B every vendor
DarkMatch and your current vendor process the identical corrupted file.
Score against the truth
Duplicates removed, connections others missed, precision, recall, F1 and household sizes, side by side.
Choose how to connect
Batch files, API, in your environment, or a dedicated clean room.
Plans
From one rooftop to a national program. Tier names, limits and pricing are being finalized.
Business
Self-serve portal, coming soon
- Upload, dedupe and household your customer files
- Files up to about 10 million records
- Standard match report
Pro
Semantic matching
- All three matching stages
- Persistent IDs and contact updates
- API and scheduled feeds
Enterprise
Your environment or a clean room
- In-your-cloud deployment on anonymous IDs
- Dedicated clean room option
- Custom volumes, terms and support
DarkMatch questions
What data formats does DarkMatch accept?
CSV, JSON and Parquet files, plus direct connections. Data can arrive by upload, SFTP, Amazon S3, Snowflake sharing or REST API. No proprietary formats or custom connectors are required.
How does DarkMatch handle the John Smith problem?
Common names at shared addresses break rules-based matching in both directions. DarkMatch compares the whole profile, including contact history, vehicles, moves and generation signals, so it links a son’s scattered records while keeping his father separate.
Can DarkMatch work with our existing matching system?
Yes. It is designed to layer on top of your current method. Many teams run it on the records their system could not resolve, or as a validation layer, before moving more matching logic over.
How do you prove accuracy?
With a known set. Start with about a million records, uploaded or created for the test, where both parties know the ground truth. The Corruptor randomly and anonymously alters copies, every method processes the same file, and results are scored against the truth. Originals are never changed.
Can we compare DarkMath with our current vendor?
Yes. Run your current vendor and DarkMatch on the same corrupted file. The report shows duplicates removed, connections found, precision and recall for each, side by side, so the comparison is objective.
Does DarkMatch need personal data?
Not always. Tests can run on hashed values, and vector-only ingestion lets you convert records to embeddings inside your environment so raw personal data never leaves it.
What is a household?
A group of people who share a residence and often share purchase decisions. DarkMatch builds households from resolved individuals, so a household is only as accurate as the identities inside it.
Find out what your current matching misses.
Send a sample or let us create a known set. Get an objective A/B report against your current vendor.