QuorumGate reviews invoices, supplier emails, and payment requests on the finance team's own devices — flagging changed bank details, look-alike senders, and abnormal amounts before money moves. High-risk cases get a four-eyes second review over peer-to-peer local inference. No cloud. No external AI API.
The most valuable review is the one finance teams can't send to a cloud AI.
Business Email Compromise drove $2.77 billion in reported U.S. losses in 2024 — the second-costliest cybercrime that year (FBI IC3). It works through a spoofed supplier email, a changed bank account, a rushed wire. The defense is to review every payment in the moment before it's signed — but invoices, bank details, and payment history are confidential and can't go to a cloud LLM. That's exactly what local AI solves.
Code decides. The model explains and can only tighten. A human approves.
Eight risk checks run in plain, auditable code — IBAN change, look-alike domain, duplicate invoice, abnormal amount, and more — and decide a verdict floor.
A local model (via the QVAC SDK) writes an explainable memo and proposes a verdict. It can only make the verdict more conservative than the floor — never loosen it.
High-value cases are delegated to a second reviewer's device over peer-to-peer local inference. An independent model returns its own verdict; only a minimal, masked bundle ever crosses.
A human makes the final decision (Approve / Hold / Escalate → Block), recorded with name and timestamp, and the desk exports an audit-evidence bundle.
Measured on real hardware, reproducible from the repo.
The audit-evidence bundle masks the IBAN and records the checks, both reviewers' verdicts, and the human's decision — no raw documents. Security invariants are encoded as tests: a high-severity finding can never produce an approval, and a prompt injection in the invoice text cannot loosen the verdict.
The offline review reproduces on one machine — no model download, no network.
# clone, install, and run the test suite git clone https://github.com/LevCey/QuorumGate cd QuorumGate && npm install npm test # review the synthetic BEC-trap sample (offline, deterministic) node packages/ui/src/desk-cli.js examples/sample-data/request-bec-trap.json
For a real local model, add --model <instruct-gguf>; for the four-eyes second review across two devices, add --peer <key>. Full quickstart in the README.
A 3-minute walkthrough: the trap, the local catch, the four-eyes split-screen, the block.
Demo video coming soon — meanwhile, run the offline review or read the source.