Precision in Interpreting Degradation
To help industries better understand how materials and systems change over time—so they can reduce uncertainty, prevent unexpected failures, and plan with confidence. We focus on extracting physics-consistent structure from complex degradation data, enabling organizations to move beyond assumption-driven interpretations toward models grounded in observed system behavior.
Our approach is built on the recognition that degradation behavior is not uniform across all conditions. Many conventional models apply rate assumptions universally, even as thermodynamic conditions evolve. The Vigil Engine™ addresses this by analyzing degradation within its thermodynamic context, allowing meaningful patterns to emerge where noise or ambiguity was previously assumed.
Rather than replacing existing engineering or scientific frameworks, our work complements them. By working across large, heterogeneous datasets, we help surface system-level behavior—such as transitions, accelerations, and stabilization—that is often obscured by locality, averaging, or limited scope.
Our Mission
Our Impact
"The Vigil Law formalizes decay by constraining rate behavior to thermodynamic regime, rather than assuming universal constancy."
What is commonly treated as irreducible noise in degradation data is often systematic distortion caused by modeling decay outside its valid thermodynamic regime. When constant-rate assumptions are extended beyond their domain of validity, predictive error accumulates silently, masked by scale and legacy confidence in established models.
By enforcing regime-constrained decay behavior, the Vigil Engine™ exposes structure that conventional approaches flatten or misattribute. Residuals tighten, trends stabilize, and regime transitions become observable rather than hidden, improving the clarity and trustworthiness of downstream analysis.
As system complexity increases, models that ignore regime validity accumulate error faster than insight. Organizations that recognize this shift early are not adopting a new methodology for its own sake—they are aligning sooner with how degradation actually behaves. The result is earlier intervention, more defensible inference, and reduced operational risk, without displacing existing physics or engineering practice.
Get in Touch
We are currently seeking candidates for OEM Pilot Integrations. Early testing reveals tighter forecasting, cleaner signals, and substantial improvements in reliability analysis—clearly outperforming traditional decay methods. Stay ahead of the curve. Reach out anytime for questions or support.
info@vigilengine.com
FAQs
What is Vigil Engine™?
Vigil Engine™ is a physics-informed prediction system designed to identify stable, meaningful structure within complex decay and degradation data. It helps organizations better understand how materials and systems evolve over time, enabling more reliable planning and fewer unexpected failures.
Which industries benefit most?
Any field where components, materials, or systems age over time can benefit. This includes batteries, turbines, semiconductors, energy systems, pharmaceuticals, industrial materials, and long-lived infrastructure—particularly where reliability, lifetime forecasting, and risk reduction are critical.
How does the technology work?
The system analyzes long-term performance and degradation data to detect consistent, system-level behavior that may be obscured by noise or local variability. An AI-assisted layer supports interpretation and deployment, translating these patterns into clear, decision-ready forecasts without altering existing physical models.
Is it easy to integrate?
Yes. Vigil Engine™ is designed to work alongside existing workflows and data sources, without requiring major infrastructure changes. API-based delivery allows teams to adopt outputs incrementally, supporting engineering, R&D, and operations with minimal disruption.
What makes it unique?
Vigil Engine™ is built on analysis first validated at geological scale and subsequently applied across multiple industrial domains. By focusing on reproducible thermodynamic structure rather than curve-fitting or local assumptions, it delivers predictions that are more stable, consistent, and transferable across systems.
Can Vigil Engine™ improve operational safety?
Yes. More reliable lifetime and degradation forecasts support better maintenance planning and earlier risk detection. By identifying emerging trends sooner, organizations can reduce the likelihood of unexpected failures and improve overall operational resilience.