Quantifiable Reliability: The Missing Metric for AI Assurance
Bigger models don't guarantee reliability. Real-time monitoring does. We provide a deterministic signal—Predictive Coherence (P)—that detects drift and distribution shifts before performance collapses.
Autonomous AI, Explained
What Self-Correction Actually Requires
The New Standard for AI Assurance
Reliability is more than optimization. It requires self-correction.
Our system uses internal stability metrics to detect misalignment and trigger adaptation—without requiring retraining or human intervention.
Why Standard Confidence Scores Fail
Current models are statistically overconfident. They often report high confidence even while failing.
Our independent assurance layer detects the "silent failures" (like sensor drift) that standard validation metrics miss.
From Output Optimization to Stability Metrics
Traditional AI chases lagging indicators like accuracy or reward.
Predictive Coherence measures the real-time coupling between agent and environment. It tells you instantly if the model is structurally capable of handling the current situation.
Automated Root Cause Analysis
A drop in performance is a symptom. Our metrics diagnose the cause.
We distinguish instantly between sensor noise (perception failure) and actuator drift (mechanical failure), enabling precise, automated mitigation.
ABOUT US
Quantifying Reliability with Information Theory
Predictive Coherence (P) provides a deterministic reliability score. It measures the real-time coupling between observations, actions, and outcomes. When P drops, reliability is degrading—signaling failure before it happens.
Download the Technical Reference
This reference paper defines the mathematical foundation for AI Assurance. It demonstrates how "Predictive Coherence" serves as a universal reliability metric, distinct from standard reward functions or confidence scores.
For technical depth, download our reference paper and query it with any LLM—Claude, ChatGPT, Grok. Ask about the math, the bounds, or how P applies to your domain.
The Information Digital Twin (IDT)
Non-Invasive Real-Time Assurance
The IDT operates as a non-invasive sidecar, running parallel to your AI without altering its architecture. It monitors the raw information flow—inputs, internal states, and outputs—to calculate real-time stability metrics.
When reliability degrades, the IDT detects it immediately. Crucially, it isolates the failure mode: distinguishing between sensor noise (observation entropy) and mechanical faults (action asymmetry) before system failure occurs.
This enables true self-correction. The system can automatically recalibrate control parameters or filter noisy inputs to maintain operational safety—all without manual oversight or model retraining.
Quantifying Human-in-the-Loop Reliability
The HDT extends our assurance framework to human-centered environments. While the IDT monitors autonomous agents, the HDT monitors the coupling reliability between a human operator and their systems—whether in cockpits, control rooms, or medical interfaces.
It analyzes the interaction stream: actions, observations, and outcomes. If the system fails to predict the operator's intent, or if the operator loses situational awareness (high observation entropy), the HDT detects this alignment failure instantly.
This enables active safety. Systems can automatically simplify interfaces during high cognitive load or alert supervisors when human-machine coordination degrades—transforming passive tools into safety-aware partners.