AI governance from training to verification
Three products built on one mathematical framework, Conservation of Fidelity. Each solves a different stage. Together they form the only end-to-end AI governance stack that covers training, inference, and verification.
Five stages, one framework
Click products to learn more. Hover support stages for details.
How data flows through the stack
Attractor Mapping — Pre-deployment screening
Before any model reaches production, screen it against domain-specific scenarios to identify failure modes, including cases where the model produces dangerous output with high confidence. Results feed directly into new verification contracts.
MCG — Training optimization
If you train or fine-tune models, MCG discovers the optimal compute allocation per layer, reducing cost by 35–78%. Layers identified as non-critical can be physically removed at inference time with near-zero quality loss.
TTU Router — Inference routing
At runtime, TTU assesses each query individually and routes it to the right-sized model. Easy queries are handled cheaply. Complex queries get the full model. 99.8% quality, 51% cost reduction. Provider-agnostic.
Safety Routing — Runtime safety layer
Detects safety-critical queries and flags cases where the model may be producing unreliable output despite appearing confident. Flagged queries are routed through CoF Audit before reaching the user.
CoF Audit — Deterministic verification
The final gate. Verification contracts evaluate AI output against deterministic safety rules. ALLOW or BLOCK, with a cryptographic audit trail. Byte-identical reproducibility. Zero external dependencies. The responsibility gate between AI output and human action.
One mathematical framework, not stitched-together tools
Every product is grounded in Conservation of Fidelity, the same mathematical framework that defines computation bounds across AI systems. This is not a portfolio of unrelated tools, it's one framework applied at different lifecycle stages.
Existing tools: pick one stage
Some do monitoring. Some do output filtering. Some do model compression. No existing tool spans training, inference, and verification. None share a common theoretical foundation across products.
FH: all stages, one theory
Conservation of Fidelity provides a unified framework for AI compute governance. Verified across multiple architectures. Each product strengthens the whole stack because they share the same mathematical foundation.
Ready to govern your AI pipeline?
We demo all three products live. No slides, just working code.