Diagnose what is actually
broken.
We open up your AI system and locate, in math, why it fails in production. The output is a written diagnosis and a remediation plan you can hand to your board or your regulator. Not slides. Not a recommendation deck. A signed forensic report.
Concrete, signed, yours to keep.
Every Applied MA pilot lands the same four artifacts. The size of the engagement (Small / Mid / Large) changes how many systems we examine — not what comes out of it.
Signed Mathematical Autopsy report
~45-page PDF. Each failure mode named, located in your stack, traced to the proof obligation that would have caught it. Cryptographically signed; verifiable on a clean machine.
Proof-obligation remediation plan
For every failure named, the math that would have prevented it. Invariants, lemmas, gates, sequenced. The roadmap your team can execute — with or without us.
Source-traceable evidence chain
Every claim in the autopsy traces to a specific line of your code or config. No assertions without provenance. Your engineers can reproduce every finding.
Executive readout + Q&A
Two sessions. One with your engineering leads to walk the technical findings. One with the board, audit committee, or risk function to walk the business consequences.
Three sizes. Time-boxed. Outcome-defined.
One production AI flow.
The single AI system that's making the most noise. Scope: ~4 weeks. One engineer pair on-site or remote, your choice.
Two to three connected systems.
The model + the retrieval layer + the action surface. Where the chain compounds. Scope: ~6 weeks.
An entire production stack.
Multi-team, multi-flow. End-to-end forensic. Scope: ~8 weeks. Joint engineering with your platform team.
At the end of every Applied MA pilot, you can answer three questions in writing: What is broken? Why? What math would have prevented it? If you can't, we didn't finish.
The work shows up as artifacts. Not slides, not abstractions.
Every engagement produces things you can hold, hand to a regulator, or run in production. Here is the list, in the order they appear.
A written Mathematical Autopsy
A forensic diagnosis of your current AI system. Every failure mode named, located in your stack, and traced to the proof obligation that would have caught it. Signed. Yours to hand to the board, the auditor, or your CISO.
A remediation plan with proof obligations
For every failure the autopsy named, the math that would have prevented it. Invariants, gates, the exact properties your system has to satisfy before it ships. This is the work your team would do if they had time to do it.
The runtime primitives, deployed in your environment
UCP, SAID, and MAE—the runtime layers that enforce the math at execution time. Sealed into your own application, or running as a managed client on your own machines. Your compute. Your network. Your keys.
An Operation Center for the system you now run
A single-tenant dashboard showing every governed call, every invariant evaluated, every gate passed or failed. Bundled mandatory with any primitive deployment. Your operators see the math working in real time.
A decision receipt for every governed call
Signed by a customer-held key, the receipt records the inputs, the invariants checked, the gates passed, the output, and a byte-identical selection replay. Your regulator can verify it without calling us.
Open Decision Receipt
Some control laws can’t be gated after the fact. They have to be proven before they run.
When the capability we autopsy is the software driving a physical system, a watch-and-log layer is useless — there is no discrete bad action to refuse, only a control law that can diverge. So we write that law in math, prove it cannot enter a runaway state, and ship it as deterministic governed runtime.
The machine moved before anyone could stop it.
At a public Children’s Day demo in June 2026, a Unitree G1 humanoid roundhouse-kicked a child; a year earlier another flailed beside a factory worker. No cause was published — and once the action is physical, there is no after to inspect.
An invariant the controller cannot violate.
A proven bound on the correction loop is not a test you hope catches the bug — it is a property the runtime carries by construction. The same primitive that proves a refund rule proves the law that keeps a machine from running away.