Start from physics
We build the real science of how assets degrade into the core of the model, so it can make a credible prediction from day one, before any failures have occurred.
PHYSICS-INFORMED AI
Asset health intelligence,
grounded in physics.
We teach AI the physics of how things fail. Nylem AI predicts how industrial assets are ageing before the damage shows itself, by building the real science of degradation into the core of the model, then letting AI make it fast, calibrated, and continuously sharper.
Physics
first, not bolted on
Day one
value before the data exists
Bounded
honest, calibrated uncertainty
Every industrial asset is quietly ageing. The damage is real, governed by physics, and almost always invisible: between inspections, inside walls, below the surface.
The blind gap
Inspect on a schedule, trust judgement in between. It works, but it leaves a long stretch where decisions rest on assumption rather than evidence.
Why not pure AI
Critical assets rarely fail. The data needed to learn failure from examples alone is exactly the data a well-run operation never accumulates. You cannot wait for things to break to learn how they break.
The Nylem path
Start from the physics of how assets degrade, then use AI to make it fast, self-aware about its own uncertainty, and continuously sharper as real data arrives. Grounding from physics. Speed and learning from AI.
From inspecting the past to predicting the future. From managing assets on assumption to managing them on evidence.
01
Physics first. Data second. Always learning.
Three ideas, in order. Each one is the reason the next can be trusted.
Calibrate with data
As real operating data and direct measurement accumulate, the model sharpens against what the asset is actually doing. Confidence narrows with every reading.
Learn continuously
The physics keeps the model grounded and explainable; the AI keeps it fast, self-aware about its own uncertainty, and improving over the life of the asset.
Prediction confidence over time
wide bounds, physics-defended
calibrated to the asset
02
Three pillars, integrated end to end.
No model predicts from siloed data. We bring operational context, direct measurement and physics into one picture.
Operational context
Integrate process data, chemistry logs, inspection records and environment into one structured environment.
Continuous sensing
Direct measurement at degradation-prone locations provides the ground truth that keeps models honest over the asset lifetime.
Physics-informed AI
Models constrained by the actual physics of degradation predict where, how and how fast failure mechanisms develop, with bounded uncertainty.
03
Nylem.
The intelligence layer: multi-layered prediction engines anchored to international integrity standards, recalibrated against measurement.
Mechanism library
Fatigue, creep, thermal cycling, wear and erosion, oxidation and corrosion, cracking and embrittlement: each degradation family is a physics module with its own governing equations.
Bounded uncertainty
Every prediction carries a calibrated interval. The platform tells you what it predicts, how confident it is, and which operating variables drive the trajectory.
Continuous recalibration
Operational data and direct measurement fold into the model continuously. Confidence narrows as evidence accumulates, never the reverse.
Standards-anchored
Outputs aligned to fitness-for-service and integrity-management standards, ready to feed the assessments your engineers already perform.
04
Bounded predictions. Auditable decisions.
Human-in-the-loop confirmation is the architecture, not an option.
Uncertainty quantified
Every prediction carries a bounded interval, calibrated to the data available: wide and physics-defended early, narrow as the asset is learned.
Human-in-the-loop
Every assessment output is issued pending. No work order releases without explicit confirmation by a qualified engineer on the operator's team.
Refuse to over-reach
Where the data is insufficient for a defensible assessment, the platform declines to specify a repair and issues a formal specialist-review request instead.
Full audit trail
Every output carries a complete traceability record: model version, dataset reference, calculation reference, confirming engineer's signature.
“The platform tells you what it predicts, how confident it is, and where it will not guess.”
05
Let's continue the conversation.
Every source. One integrity picture. Continuously.
Prefer email? info@nylem.ai