Reproducible
The same inputs return the same decision, every time. No drift between the demo and production.
Mathematically Governed AI
Ship theAI Wealth Advisorthat's stuck in pilot.
We make AI prove what it will and won't do, in math, before it runs — and let anyone who has to sign off verify it. Try to get a bad action through — then check the proof yourself.
The clock is runningColorado SB 26-189Jan 2027EU AI Act · high-risk2027
EU AI Act · Reg. 2024/1689Colorado AI Act · SB 26-189NYDFS · CL 2024-7NAIC · Model BulletinNIST · AI RMF 1.0ISO/IEC · 42001FDA · AI/ML SaMDHIPAA · 45 CFR 164SR 11-7 · Model RiskSOC 2 · Type II
The problem · why pilots stall
Six failures. One root cause.
These aren't six problems — they're six symptoms of one: AI is built backwards. Written first, tested after, hoped about. You cannot govern a guess with another guess. See why that's the root cause →
F1 · Prove
Compounding error
Six chained steps at 95% each land at 73%. Agents take dozens of steps — the error compounds, and no prompt fixes exponential decay.
F2 · Replay
Production drift
Right in the pilot, drifted in production. The action fires before the dashboard alerts — you find out after it shipped.
F3 · Bind
Eval-set rot
Your scores stay green while production fails, because the eval set stopped resembling the real world months ago.
F4 · Prevent
Governance after the fact
The control logs the destructive action after it ran. A log is a post-mortem, not a control.
F5 · Specify
No proof object
Challenged on one decision eighteen months later, a guess can't show what it did — only a log it wrote about itself.
F6 · Lead
Policy is not evidence
Regulators stopped accepting a document. They expect the control operating in production, with evidence it ran.
The method
We prove the math before we write the code.
We prove the math before we write the code. It is the inversion of how software is built today: instead of writing code and then testing and hoping, you start with what must always be true, prove it, and compile the proven rules into the app as constraints it cannot break. We call the discipline the Mathematical Autopsy.
01
Define the intent as math
Write down what must always be true — the invariants the system can never violate — as formal statements, not prose policy.
02
Draft and check the proof
An AI proof-drafting assistant proposes the math and the proof; the Lean 4 kernel — a public proof system — checks it. The AI does the flexible part; the math is what we trust.
03
Compile the rules in
The proven rules become constraints compiled into the code — not a guardrail bolted on top, but part of how the software is built.
04
Gate the action, sign the decision
The gate runs before the action fires, enforced at every step. Each decision writes a signed Decision Receipt your auditor can replay on a clean machine.
The old way
write code → test → hope→Oursprove the math → compile → enforceThe fair question
“So the proof is only as good as the rule.” Correct.
Formal proof guarantees an action can never violate the rules as you encode them. A standard like fair lending isn't one rule — it's a set of them, and we formalize and prove each. We don't claim a single theorem captures an entire law in one stroke; the real work is decomposing the standard into the right, complete set of invariants — the most scrutinized thing in the system.
01
Authored with your experts
Invariants are written with your own risk, compliance, and legal teams — the people who already own the policy. We encode their rule; we don't invent it.
02
Inspectable, not buried
The rule is a proof anyone can read and re-check in the public Lean kernel — not a model weight or a prompt no one can audit. If it's wrong, it's wrong in the open.
03
Versioned and on the record
Every receipt names the exact rule version that fired. Change the rule and the change is dated, attributed, and replayable — so coverage gaps surface instead of hiding.
Moving the hard part from “trust the model” to “get the rule right, in the open, with the people who own it” is the entire point. A guess you can't inspect becomes a rule you can argue about, fix, and prove.
The outcome
Mathematically governed software. Provable by construction.
The model still proposes — any model, swappable, frontier or open. What we make deterministic is the governed decision: the same input produces the same governed output, every time. That is what makes it provable — to the regulator, the auditor, the compliance officer who has to sign off. Not “we think it's safe,” but here is the proof, re-run it yourself.
Seven properties — by construction, not by promise
Traceable
Every decision names the rule that governed it and the inputs it saw. Nothing happens off the record.
Explainable
The reason is the proven invariant itself — not a guess at why the model did what it did.
Auditable
Each decision ships a signed receipt your auditor can open, read, and check on their own.
Replayable
Feed the recorded inputs back through the sealed runtime and get the identical decision out.
Falsifiable
If a rule would be violated, the action is refused. The guarantee can fail loudly, not silently.
Verifiable
Signed with a key we don't hold, so anyone can re-derive the proof off-platform, without us.
The beachhead — not the ceiling
Where it bites first.
We start where the pain is sharpest: regulated enterprises moving AI from demo to production — banking, insurance, health plans, where no one signs off on "right 99% of the time." The same gate you just attacked governs every one of them.
The operators
Five operators. One guarantee.
Prove the action, make it deterministic, sign the receipt — across both sides of where AI lives: the agents reaching out, and the AI you build in. All inside your environment, your keys. How they fit together →
AICPAI Control Plane
Gates every AI action — admits or refuses it before it fires, with a signed receipt either way.
The gate · admit or refuseAICP →
SAGESealed AI Governed Engine
Same input, same governed output — every call replayable and signed, verifiable by your auditor.
Deterministic · replayableSAGE →
MAEMathematical Autopsy Engine
The engine you build proven software with — run the autopsy method in your own pipeline.
The build engine · powers MGRMAE →
MGRMathematically Governed Runtime
Your hardest capability as a governed, deterministic runtime — proven in math, then yours to keep.
Your hardest capability, provenMGR →
OC · Operations Center
The authority layer over every governed runtime.
Every receipt, fleet-wide, becomes a board- and regulator-ready evidence package — so you lead with proof, not policy. Deploys with any enterprise rollout.
Operations Center — every receipt, every runtime, fleet-wide. Live governed activity, fleet convergence, and a board-ready evidence trail.
Start free
Run governed AI on your own machine today.
Studio Loge is free, forever. Download it, point it at your flow, and watch every action get a signed Decision Receipt — no sales call required. Enterprise adds Operations Center and fleet-wide evidence.
Ship it with proof.
“You cannot govern a guess with another guess.”
The clock is running — Colorado SB 26-189 · Jan 2027 · EU AI Act high-risk · 2027