Self-hosted deployment
Runs entirely on infrastructure you control.
Consequence AI Systems builds governance infrastructure for AI systems operating in regulated environments — starting with ARBITER, a self-hosted control layer for financial and insurance workflows.
Cloud and AI spend has become a governance problem, not just a cost problem. AI workloads are dynamic, distributed, and tied to engineering decisions made every day — after-the-fact approvals no longer keep up. Security and compliance are now the top-cited concern for organizations scaling AI.
We build for exactly that intersection: control, auditability, and cost governance for AI, enforced where it can be proven rather than promised.
What's working today in ARBITER, demonstrated live — not a roadmap slide.
Runs entirely on infrastructure you control.
Proven in both directions, at the database layer — demonstrated live.
Every AI call produces a tamper-resistant, tenant-scoped audit record.
Block, allow, or require human review, based on your rules.
AI spend governed per call, before it happens.
Every result scored against your rules, verdict recorded.
ARBITER is a working prototype built by one person. It is not certified, production-hardened enterprise software today. The path to that — SOC 2, third-party penetration testing, BAA capability, the full audited control set — is deliberate work ahead, not something we'll pretend is finished. What exists is the hard architectural core, working and demonstrable. The rest is a roadmap.
Self-hosted AI governance for regulated financial and insurance workflows — tenant isolation enforced at the database, policy and constitutional checks before and after every model call, and signed execution certificates as proof of what happened.
Future governance products for regulated industries will follow the same architecture: enforced where it can be proven, not promised.
Independent founder. ARBITER started from a simple frustration: trying to find real work in a marketplace flooded with scams and spam, and realizing the hard problem wasn't the work — it was separating what's real and trustworthy from what isn't. That question pulled me into the deeper one underneath today's AI boom: when a machine does the work, how do you trust it, govern it, and prove what it did?
I built ARBITER to answer that for the people who can least afford to get it wrong — regulated financial and insurance firms. I build the careful way: nothing is called done until it has been run and verified.
I'll show you the working demo live — two tenants, complete isolation, a real AI request flowing through the full governance pipeline — and I'll be straight about exactly what's built and what isn't.