Quantifiable Reliability: The Missing Metric for AI Assurance
Bigger models don't guarantee reliability. Real-time monitoring does. We provide Bi-Predictability (P)—a metric measuring how well an agent and its environment predict one another. P flags drift and distribution shifts that accuracy and reward signals miss.
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.
Bi-Predictability measures the real-time coupling between agent and environment: how much information is being utilized to take actions with respect to total available information. It tells instantly if the agent model is structurally capable of handling the current environment.
Automated Root Cause Analysis
A drop in performance is a symptom. Our metrics help locate the cause.
​
​With paired sensor and actuator records, the components of P help separate perception faults from actuator faults, so mitigation can be targeted.
ABOUT US
Quantifying Reliability with Information Theory
Bi-Predictability (P) provides a deterministic reliability score. It measures the real-time coupling between the AI agent's observations, actions, and outcomes. When P moves away from the system's own healthy baseline, the interaction is degrading, often while task scores still look normal.
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.
​
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.
​