Pre-seed · Stockholm

Train better. Serve cheaper.
Verify safer.

Fidelity Horizon builds governed AI infrastructure, three products that make AI models more efficient to train, cheaper to serve, and safe to trust. Verified across multiple architectures from 11M to 72B parameters.

Book a demo →Calculate your savings
Inference cost reduction
51%
Quality retained
99.8%
Training FLOP reduction
35–78%
Provisional patents
23
What we build

Three products, one governed stack

Each solves a distinct problem in the AI lifecycle. Together they form a complete governance pipeline from training to verified production output.

Training

MCG

Engine tuning for AI models. Identifies which parts of a neural network are unnecessary and removes them. Verified across multiple architectures from 11M to 72B parameters.

35–78% FLOP reduction · Zero quality loss at layer removal (72B)Learn more →
Inference

TTU Router

An automatic gearbox for AI inference. Automatically determines whether each query needs an expensive model or a cheaper one, based on each response, not manual rules. Provider-agnostic.

51% cost savings · 99.8% qualityLearn more →
Verification

CoF Audit

The flight control system for AI. Every decision checked against safety contracts before it reaches a human. Deterministic, reproducible, model-agnostic. EU AI Act ready.

103 tests · 8 example contracts · 0% false GREENLearn more →
How it works

From training to verified output

Pre-deployment screening finds model failures. Runtime routing optimizes cost. Deterministic verification catches what confidence misses.

Attractor mapping
Pre-deployment screening. Identifies failure modes, including cases where the model is confidently wrong. Results feed into new verification contracts.
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MCG training
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TTU routing
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Safety routing
Runtime safety layer. Detects safety-critical queries and flags cases where model output may be unreliable. Flagged queries route through CoF Audit for verification.
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CoF Audit

Each component works standalone. The stack multiplies savings.

The problem

AI is expensive, opaque, and unverified

$8.4B+
Enterprise LLM spending in 2025

Most organizations overpay by 40–60% because every query goes to the most expensive model regardless of difficulty.

0%
Of AI responses deterministically verified before delivery

Models produce answers with no quality check. In regulated industries, that is a compliance gap waiting to be exploited.

Aug 2026
EU AI Act enforcement begins

High-risk AI systems require robustness verification with reproducible audit trails. Most AI deployments have no deterministic verification layer.

Intellectual property

23 provisional patents filed

23 provisional patents protect the theoretical foundation and product implementations across all three products.

Ready to govern your AI?

We're looking for design partners for pilot deployments.

Book a demo →See the full stack