Products
The toolchain behind Mathematically Governed AI.
We do not lead with products. We lead with advisory. Products pull through behind engineering, inserted into the account by demonstrated need.
How we deliver
Advisory opens. Engineering builds. Products support.
Stage 1
Advisory — Mathematical Autopsy
Every engagement opens here. A forensic, math-first diagnosis that shows the customer exactly where the math is not.
Stage 2
Forward-Deployed Engineering
Our engineers embed on-site to convert the diagnosis into a running, governed system.
Stage 3
Product Deployment
As engineering work uncovers needs, governed components are deployed into the account.
Ready for proof of concept
Two components real enough to deploy today.
Both are built on the same proof discipline. Neither is generally available yet, and we would rather be honest about that than pad the page.
Ready for POC
UCP — Universal Control Plane
The governance and policy layer that sits over an AI system in production and enforces the invariants proven during Mathematical Autopsy. If a guarantee stops holding at runtime, UCP fails loudly, routes around the failure, and writes the audit trail a regulator can query.
Built for organizations that need to demonstrate, not assert, that their AI is behaving inside the constraints they committed to.
Ready for POC
SAID — Self-Aware Intent Director
The agent runtime. Where intent gets turned into bounded, replayable execution under the gates that came out of the Autopsy. Every step a SAID agent takes is traceable back to the theorem that allowed it, which is what makes incident forensics possible instead of speculative.
Designed for teams who need agents that can be audited line-by-line, not just monitored in aggregate.
Coming soon
The engines underneath, packaged for buyers.
The engines are real and running in research. The packaged products that wrap them are not ready yet. When they are, they will appear here with the same honesty about status.
Storage substrate
Memory that holds proofs alongside meaning, so retrieval returns evidence and not just an embedding lookup.
Intent engine
Turns ambiguous requests into measurable intent fields the rest of the stack can route, gate, and prove against.
Orchestration engine
Routes intent into governed action across services, with runtime invariants that fail loudly the moment a guarantee stops holding.
Meaning extraction
Decomposes language into structured meaning so the system operates on semantics and roles, not just token sequences.
Want to see one of these in production?
Every product conversation starts with a Mathematical Autopsy. The fastest way to understand what we ship is to watch us run one on yours.