AI governance from training to verification
Fidelity Horizon's AI governance stack is a unified pipeline of three products — MCG, TTU Router, and CoF Audit — built on the Conservation of Fidelity mathematical framework, covering training optimization, inference routing, and deterministic verification. Each component works standalone. The stack multiplies savings.
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.
Governance stack — Common questions
What is an AI governance stack?
An AI governance stack is a unified set of tools that governs AI systems across their entire lifecycle. Fidelity Horizon's stack covers training optimization (MCG), inference routing (TTU Router), and deterministic verification (CoF Audit) — all grounded in the Conservation of Fidelity mathematical framework.
Do I need all three products or can I use them individually?
Each product works standalone. MCG optimizes training independently, TTU Router reduces inference costs as a drop-in proxy, and CoF Audit verifies AI outputs without requiring the other products. The stack multiplies savings when used together.
What is Conservation of Fidelity?
Conservation of Fidelity is the mathematical framework developed by Fidelity Horizon that defines computation bounds across AI systems. It provides the theoretical foundation for all three products, ensuring they share a common mathematical basis.
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