Certezaħ applies fundamental science to build world models that quantify AI uncertainty. Warning before systems fail, not after.
As AI agents proliferate, making decisions, triggering actions, coordinating with each other, their failures become invisible. An agent that's uncertain doesn't raise its hand. It guesses. And when multiple uncertain agents pass information to each other, small doubts compound into confident-sounding catastrophes.
Nobody monitors the space between agents. Not the confidence of handoffs. Not the information lost in translation. Not the false certainty that compounds across each step.
Agents get stuck in infinite loops. They hallucinate facts and pass them downstream as truth. They amplify each other's errors until the system outputs a confident answer built on nothing. And the more autonomous they become, the harder these failures are to catch.
We catch them.
No LLM-as-a-judge. No domain knowledge required. Pure signal analysis from entropy, divergence, and information theory.
Measures whether an agent is guessing or certain. A flat probability distribution is a red flag, regardless of what it says.
Checks if agents tell the same story. When a retriever and a generator diverge semantically, trust should decrease.
Tracks how much context is dropped at each handoff. If 80% of information disappears between agents, that's a measurable risk.
Detects when the system manufactures certainty. 0.7 × 0.8 upstream should not produce 0.95 downstream. Pure math.
Learns what normal looks like. Flags when agent patterns deviate from baseline, like statistical process control for AI.
Most observability tools use LLMs to judge LLMs, inheriting the same failure modes they claim to detect. We build world models from telemetric signals, using the same mathematics that describes uncertainty at the quantum level.
Certezaħ is preventive, not reactive. We don't report what went wrong. We warn before it happens.
Think of it as TÜV / UL for AI agents. We don't build them. We certify they're safe.
Trained on telemetric data, not language
Same hallucination blind spots as the system it evaluates
Warns before failure happens
Reports failure after the fact
Monitors the space between agents
Doesn't see coordination failures
Transparent scores you can audit
Trust us, the AI said it's fine
No LLMs in the monitoring loop. Mathematical signal analysis runs at a fraction of the cost of model-based evaluation.
Supports regulatory requirements for high-risk AI systems with auditable uncertainty metrics and transparent reporting.
Prevent failures before they cascade. Catch infinite loops, hallucination amplification, and confidence drift in real time.
Understand your agent systems deeply: where they're certain, where they're guessing, and where the risks are. No more opaque pipelines.
We aim for wu wei, effortless action. A seamless state where humans and AI work together without friction or doubt, where manual interventions are minimized and constant adjustments become unnecessary.
This state cannot be forced. It naturally emerges only when built on a foundation of absolute safety, reliability, and trust, where both sides are aware of their limits. AI that is receptive enough to know what it doesn't know, and humans wise enough to trust the signals. Certezaħ provides that foundation, so you don't wrestle with the AI; you simply flow with it.
We're working with design partners to shape the first trust layer for multi-agent AI systems.