Finding the extraordinary.
A targeting engine for US talent visas.

01What the data can see — 8 USCIS criteria

Extraordinary-ability cases are won on a checklist: meet ≥3 of 8 criteria. Our database carries direct proxies for some, weak proxies for others, and is honestly blind to a few. We build the funnel on what we see strongly, and attach the sparse signals as evidence bullets.

02The signal catalogue — what we can search by

USCIS criteria are what qualifies a person. Signals are how we find them in the data. Our platform detects 37 signals across the graph; here are the families that matter for visa targeting — grouped by what they tell you.

03Who we target — three intent segments

The buyer (a visa agency) needs people who want to enter the US. So the hottest candidate is not someone already there — it's someone in motion. We separate three populations by intent heat.

04Where the relocators are

The 239,221 relocators, by current country. Georgia, Armenia and Kazakhstan are the dominant hubs — exactly the post-2022 relocation map.

05The scoring engine — Tier A / B / C / D

Every candidate is scored on how many independent USCIS criteria they hit, then ranked into tiers. This is the core IP: not a flat list, but a prioritised one where the attorney-ready names float to the top. (Distribution below measured live on the relocator pool.)

06Funnels — how a pool becomes a shortlist

Each segment narrows from raw population to the sellable intersection: visa-signal ∧ contactable. The shape differs by segment — relocators are a huge hot pool with thinner DB contactability; the US case is smaller but far more reachable.

Relocators-2026 — the primary funnel

US-diaspora — the change-of-status case (leadership-dense)

Inside RU/CIS — the largest base

07Change-triggers — monitoring, not snapshots

The strongest product isn't a static list — it's catching people the moment they become visa-ready. Someone just promoted, just published, just defended a PhD is exactly when a US visa enters their mind. We can monitor these transitions and surface fresh candidates continuously.

The timestamped fields we diff over time

Worked example — a Tier-C engineer becomes Tier-A

08The most valuable signals — our ranking

From everything measurable, these are the signals we'd weight highest for finding a visa agency's next client — ranked, with the reason. Change and intent beat static evidence: a "strong profile" is everywhere; a strong profile that just moved or got promoted is a warm lead.

09Custom depth — what we build per buyer

The numbers above are the general picture. For a specific buyer we narrow hard — by city, company, field, funding, or nationality. Each of these is a query away on the same engine.

10One candidate, fully assembled — ~130 fields

Aggregate counts are abstract. Here is what we hold on one real person (anonymised) — a typical tech relocator. This is the depth behind every row in the funnels above: not a name and a title, but a fully-reconstructed dossier an attorney can act on.

Cross-source depth — one person across professional profile + open source

For technical candidates we join a second graph: their open-source footprint. Here is Profile #B-2026 (anonymised) — a tech relocator whose code we can read field by field.

11We verify before we sell — content sanity

Before quoting a buyer we read the actual data, not just the counts. A signal that exists is not the same as a signal that qualifies. Here is what eyeballing real records revealed — and why it changes how we score.

12Guardrails — sold responsibly

This product is viable behind clear lines (final wording needs an attorney sign-off, not this analysis):

• Flat per-lead / fixed subscription — never %-of-engagement (ABA 5.4/7.2).
• Framing: "public-profile evidence mapped to USCIS criteria" — never "qualifies for a visa" (UPL).
• OFAC/SDN screening on every batch; geo-scope GDPR/CCPA; data-broker registration.
• Tiers are evidence-strength, attorney-to-verify — not eligibility verdicts.