SPHINXX.AI

Verification infrastructure for AI. Built to fail safe.

When AI computation runs on machines nobody fully controls, there is no practical way to confirm the work was real — or that the machines were. SPHINXX.AI builds the patent-pending protocol layer that verifies it, and refuses to release what it can't verify.

Adversarially pilot-tested, June 2026real machines · 67% adversarial saturation · failed safe by design

The problem

Distributed and outsourced AI compute carries a 25-year-old fraud class: workers that claim results they never computed, and single operators masquerading as thousands of machines. In one 2024 incident, roughly 1.8 million fabricated GPU identities flooded a commercial compute marketplace. The industry's answer — paying for the same work two or three times over — is a tax on the entire distributed-AI economy.

The protocol

A three-layer verification protocol — patent-pending; portfolio managed by national patent counsel.

01

Sealed commitments

Every machine cryptographically commits to its result — anchored to hardware identity — before any peer's result is visible. No copying, no last-mover advantage.

02

Hidden verification

Known-answer checks are woven invisibly into real work, scheduled continuously against each machine's earned trust. Less trust, more scrutiny.

03

Trust-weighted release

Results release only when verified, trust-weighted consensus clears the bar. Otherwise the system withholds — and escalates to a human.

The defining property: under successful attack, the protocol's failure mode is refusing to release — across the tested envelope, integrity-stressed decisions were withheld and escalated rather than confidently released, with the higher-saturation capture boundary mapped as the primary open research problem. A complementary pre-conclusion gating approach applies the same discipline to AI conclusions generally: human authority, preserved by architecture.

Validation to date

100% / 0%
detection / false positives at 67% adversarial saturation, verification tasks hidden — real, physically separate machines
Failed safe
worst case (adversaries detect the checks): 98–100% of decisions withheld and escalated to a human
3 phases
simulation → four heterogeneous physical devices → adversarial saturation series, April–June 2026

Inventor-run pilot results. Independent replication at 10× scale is the next validation step.

Read the public results brief (PDF) →

Status

SPHINXX.AI, LLC is an independent North Carolina company. The protocol portfolio is managed by national patent counsel; a federal SBIR pitch is under agency review; independent university replication of the adversarial pilot is being pursued. Pilot partnerships with distributed-compute platforms and regulated AI deployers are welcome.