Mathematical Autopsy
Everyone inspects the building. Nobody verifies the blueprint. SMARTHAUS proves the blueprint is correct before construction begins.
We prove, then build.
The Blueprint Problem
A builder takes a blueprint and builds against it. Inspectors check if the building matches the plans, the rules, and local laws. But nobody checks whether the blueprint itself is correct. The entire process can produce a validated structure that doesn't match what the blueprint was supposed to guarantee. Most AI governance companies verify code after the fact — they're inspectors. SMARTHAUS prevents the blueprint from ever being wrong. We prove the math before code exists, so everything built against it inherits those guarantees by construction.
What Mathematical Autopsy Delivers
YAML specifications with mathematical constraints
Executable proofs with deterministic results
Same inputs always produce same outputs
The 8-Stage Pipeline
Every piece of math/algorithm work passes through all eight stages. No shortcuts. No skipping. The pipeline is the guarantee.
| Stage | Name | What Happens |
|---|---|---|
| 1 | Intent | Problem statement and success criteria in plain language. Stakeholders align on what we are proving and why. |
| 2 | Formula | The governing mathematical relation. One equation that captures the input-output contract for the scope. |
| 3 | Calculus | Formal operators, complexity analysis, and implementation notes. The math is made precise and computable. |
| 4 | Lemmas | Formal claims with proof sketches. Each lemma is a mathematical guarantee that the system must satisfy. |
| 5 | Invariants | Machine-enforced constraints in YAML. Violations block deployment. These are the non-negotiable rules. |
| 6 | Notebooks | Executable verification notebooks prove every lemma holds. Deterministic execution with fixed seeds. Reproducible by anyone. |
| 7 | Scorecard | Aggregate pass/fail across all invariants and notebooks. Go/No-Go gate. No scorecard pass, no deployment. |
| 8 | Extraction | Proven notebook code extracted to production. Zero-drift guarantee: what was proved is what ships. |
Powered by Leanstral
Our proof engine is built on Lean 4—the same formal verification language used by Fields Medal mathematicians to verify their proofs. Leanstral is our integration layer that connects the Mathematical Autopsy pipeline to Lean's theorem prover.
When we say "proved," we mean it in the mathematical sense: a machine-checked proof that the system satisfies its specification. Not a test suite. Not a benchmark. A proof.
The same rigor that verifies number theory and algebraic geometry now verifies your AI governance contracts.
Everyone Else vs SMARTHAUS
The difference between testing after the fact and proving before the fact.
| Aspect | Everyone Else | SMARTHAUS |
|---|---|---|
| When math happens | After code is written (if at all) | Before code is written |
| Verification method | Unit tests, integration tests, hope | Formal proofs, invariants, executable notebooks |
| Proof tooling | None | Lean 4 via Leanstral (same tool Fields Medal mathematicians use) |
| Deployment gate | Tests pass in CI | Scorecard pass: all invariants validated, all lemmas proved |
| Drift detection | Manual code review | Zero-drift extraction: notebook-to-code is automated and validated |
| Audit trail | Git history and ticket references | Complete mathematical provenance: intent to formula to proof to code |
| Determinism guarantee | Best-effort testing | Seeded execution, reproducible artifacts, HMAC-signed receipts |
Who Needs Mathematical Autopsy?
Regulated Industries
Complete audit trail from requirement to implementation. Every decision traceable and provable for compliance.
Financial services, healthcare, and government sectors require complete provenance tracking. Mathematical Autopsy provides immutable evidence chains that satisfy regulatory audits. Every operation links back to formal specifications, making compliance verification straightforward and defensible.
Critical Systems
Mathematical proof before deployment. No ambiguity in safety-critical implementations.
Aerospace, medical devices, and autonomous systems cannot tolerate uncertainty. Mathematical Autopsy ensures that every behavior is mathematically guaranteed, not just tested. Invariants block deployments that violate safety constraints, preventing catastrophic failures before they reach production.
AI Engineering
Transform complex AI workflows into deterministic pipelines with guaranteed behavior.
AI systems often behave unpredictably due to non-deterministic components and complex interactions. Mathematical Autopsy decomposes AI workflows into provable mathematical operations. Each component has formal specifications, enabling engineers to reason about system behavior and guarantee outcomes.
Enterprise Trust
Build confidence through transparency. Every output grounded in an intermediate symbolic step.
Enterprise adoption requires trust that systems will behave as expected under all conditions. Mathematical Autopsy provides transparency through formal proofs and executable verification. Stakeholders can verify guarantees themselves rather than relying on vendor claims.
You Don't Have to Trust Us
You can verify the mathematics yourself. Every guarantee is publicly documented, formally proven, automatically enforced, and validated before deployment. This is the difference between "we tested it" and "we proved it."
Ready to Prove Your AI Works?
Mathematical Autopsy transforms black-box AI into transparent, provable systems. Every decision traced, every output verified.