In January 2023, the U.S. Department of Justice charged 25 people in a scheme that sold more than 7,600 fake nursing diplomas from three Florida schools. The buyers used those diplomas to sit for national board exams, obtain state licenses, and get jobs in hospitals across at least nine states – nurses who had never completed clinical training, treating real patients. The federal investigation, dubbed “Operation Nightingale,” is still active: a second wave of charges landed in 2025. The scheme only came to light through a federal enforcement action, not through any built-in safeguard in the credentialing system itself.

This is what happens when credentials live in disconnected silos, verified by manual processes that assume documents are genuine because they look genuine. Every credential issuer – every university, employer, certification body – operates its own database with its own format and its own verification mechanism (if any). There is no common standard, no universal way for a third party to independently check authenticity, and no mechanism for the holder to control where and how their credentials are shared. The result is a multi-billion-dollar background check industry built on the difficulty of doing something that should be straightforward.

We built a protocol and working proof of concept that takes a different approach. It combines W3C Verifiable Credentials v2.0 – an open web standard for machine-verifiable credentials, finalized in May 2025 – with blockchain as an accountability layer for anchoring credential hashes and managing revocation. We’re publishing a research paper with the full protocol design, architecture, cross-chain comparison, and results, alongside the source code on GitHub.

When one company hires another to build software, a familiar problem arises at the point of delivery: does the work actually meet the specification? Today, that question is answered through manual review — meetings, back-and-forth, and sometimes expensive third-party arbitration. It’s slow, subjective, and scales poorly.

We built a proof of concept that takes a different approach. It combines blockchain-based smart contract escrow with an AI agent acting as a third-party verifier. The client deposits funds into a smart contract. The contractor submits their work. An AI agent evaluates the deliverable against the agreed requirements — building it, running it, testing it, and scoring its compliance — then records the result on-chain. Depending on the score and the AI’s confidence, the contract automatically releases funds, notifies the contractor to fix and resubmit, or escalates to a human arbiter.

We’re publishing a research paper with the full concept, architecture, and results, alongside the source code on GitHub.