// Pumps AI Lab
Where pump physics meets machine learning.
First-principles simulation built our training ground. Physics-informed learning is what makes it fast enough to run live on your pump. This is the research engine behind Accel and Sentinel.
Accel + Sentinel
First-principles
Field-hardened
// Simulation-forged, field-hardened
Built on the physics, hardened on reality.
Our models were forged on a corpus of physics simulations spanning every fault mode and fluid regime — now being enriched and validated with real-world operational datasets.
Discipline rule: the number is “about 72,000 simulations” — never inflated, never implying field deployments we don’t have.
// Two methods, one physics
Accel for breadth. Sentinel for depth.
The same governing equations, applied two ways — matched to what each asset’s risk and budget justify.
A neural network whose loss function penalises violations of physical equations, so it learns physically consistent solutions. Physics modules stand in for sensors we don’t have — inferring internal state from minimal external signal.
- Physics compensates for absent sensors
- Real-time, low-cost, fleet-scalable
- Confidence-scored inference
Models whose architecture embeds physical laws rather than learning purely from data. Waveform-grade inputs feed a physics engine of distinct modules that explains the subtlest degradations with full root-cause attribution.
- Physics-derived features, every drift attributable
- Root-cause mapping in the pump’s actual physics
- Confidence scores plus RCA
// The quiet hero
The Baseline Performance Curve.
The physics-derived signature of how a specific pump should behave across its operating envelope — established at commissioning, or at manufacture for Twin-at-Birth OEMs. It is the lifetime reference for all intelligence.
Every differentiated claim traces back to it: energy anomalies, fault signatures, slip degradation. Day one, your pump gets its baseline. Every day after, we measure reality against it — drift has nowhere to hide.
// Disambiguation
What we are — and what we’re not.
Our team will be asked “is this like Physical AI?” Getting these distinctions crisp is a credibility moment. We embed exact physics about one thing: PD pumps.