Identity resolution data that catches a false claim without slowing down a real one.
Identity resolution data only earns its place in a risk model if it catches a fabricated claim and leaves a legitimate user alone. Most fraud tools are better at the first than the second, which is how real customers end up declined by mistake. We match 250+ fields per person record, refreshed hourly. An employee count, a credential, an identity: each one gets checked against current, verifiable signal instead of a stale or incomplete record that produces a false positive.
Representative of the production index, not a real customer.
Six fragments in. One identity out.
Partial signals (an email here, an employer there) collapse into a single verified person against a 1.20B-profile match graph, scored as each source links.
No single source is enough. The graph is: six partial signals resolve to one person in ~1.2 seconds, with a confidence score you can threshold on.
- email✓j.okafor@northwind.iomatched
- profile✓@jane-okaformatched
- employer✓Northwind Roboticsmatched
- code✓@jokafor · 41 reposmatched
- history✓3 employers · 9 yrmatched
- your CRM✓cust_88421matched
- resolved → identityJane Okafor · 1.2 s
Illustrative, resolved live from the production index, not a real customer.
Resolution and verification are two different problems.
Identity resolution matches fragments, an email, a name, a company, a social handle, into one confirmed profile. Identity verification is what happens next, checking a specific claim against that resolved profile. We do the first directly and enable the second. Once a profile is resolved, a claimed employee count, a claimed credential, or a claimed identity, has something real to be checked against.
Reduce fraud without adding friction for legitimate users
Identity verification false positives happen when a system is too aggressive with too little real signal. A legitimate user gets declined because the checks available couldn't confirm what was actually true. Our hourly refresh closes that gap. A verification check runs against current data, not a snapshot from months ago, so the line between “fraudulent” and “just needs a fresher record” gets clearer instead of blurrier.
Verify a claimed employee count against what's actually true
How do you verify a company's employee count during onboarding? A company claiming 50 employees is a fraud signal on its own if the real data shows one or two people actually work there. Business verification data built this way doesn't require a new process. It checks an existing claim against an independently tracked headcount. That is the same signal used elsewhere on this site for growth and hiring, applied here as a fraud check instead.
Confirm a submitted identity is a real person
Government agencies use this the same way a KYB platform uses employee counts, cross-checking a submitted email or identity against independently tracked contact and professional data, rather than trusting a form submission at face value. A real, resolvable profile behind a submitted identity is a strong signal on its own; a submission that resolves to nothing is worth a second look.
Vet a bidder before a high-value transaction clears
How do marketplaces verify high-value bidders? An auction platform uses our data for exactly this, confirming a bidder's professional identity and standing before allowing a high-value transaction to proceed, rather than after a dispute. The check happens once, before the transaction, not as after-the-fact fraud recovery.
Cross-check a claimed certification against real signal
Professional membership and association organizations use our data to check a member's claimed credentials against their real professional record, catching someone claiming a certification they do not actually hold. It's the same underlying mechanism as the employee-count check, applied to a different claim for a different buyer.
How a partial profile becomes a verified identity in seconds.
Ingest partial data
fragments in
Match the graph
1.20B+ people profiles
Score & verify
confidence + risk flags
Return in seconds
API · subscription
Where this shows up in a real product.
- KYB Employee-Count Verification. Check a claimed headcount against real employee data, a mismatch is a fraud signal on its own.
- Bot & Real-Person Verification. Cross-check a submitted identity against independently tracked contact and professional data.
- Marketplace Bidder Vetting. Confirm a high-value bidder's professional standing before a transaction clears, not after.
- Credential & Certification Verification. Check a claimed certification against real professional signal.
Match accuracy over match volume.
Detecting a synthetic identity with third-party data means checking whether the pieces actually cohere, not just whether each piece exists somewhere. Our 250+ fields per person record give a risk model enough surface area to catch that kind of inconsistency. A profile that is thin, contradictory, or unresolvable across sources is itself a signal, separate from any single field being individually plausible.
Verify the claim without punishing the real user.
Book a 20-minute call. Bring a use case, a claim type, or a false-positive rate you're trying to fix, and we'll show you what's actually current.
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