OmniPath – Adaptive Deep Reinforcement Learning for Complex Assets & Systems

OmniPath is an adaptive deep reinforcement learning platform for complex assets and systems

Our proprietary adaptive deep reinforcement learning models learn how your equipment actually behaves — then continuously optimize performance, efficiency, and emissions in real time, from cloud to edge. Self-improving quantitative models built for complex systems — not language-based models.

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The problem

Static models drift. Assets don't stand still.

Heavy-asset industries lose $1 trillion+ annually to unplanned downtime, sub-optimal set-points, and manual intervention. Traditional optimization tops out at 3–5% efficiency gains before models drift and degrade — and rising energy costs and carbon pricing punish every point left on the table.

Rules & static ML fall behind

Rule-based and supervised ML models struggle with dynamic, non-linear physics and changing operating envelopes — ambient temperature, load, and product composition shift faster than models can be retrained.

Every asset is different

Fleet-level models miss asset- and site-specific variability. Constant manual retraining makes the ROI unappealing for most industrial operators.

Carbon costs compound losses

ESG pressure, rising energy costs, and carbon pricing demand continuous efficiency gains and lower emissions — not one-time tuning projects.

Platform

Adaptive intelligence, delivered as a service

OmniPath integrates three capabilities into one platform — the outcome is self-operating assets that learn, predict, and optimize system behaviour in real time.

Adaptive Deep Reinforcement Learning Models

We train intelligent control models — per component, per site — that learn optimal responses to dynamic environments, reducing downtime and optimizing performance without frequent manual retraining.

Full-Fidelity 3D Collaboration & Visualization

Built on NVIDIA Omniverse and the OpenUSD standard: immersive, multi-user environments for engineering, analysis, and decision-making driven by live or simulated data.

Edge / DMZ Deployment

First-in-class, simplified large-model deployment through a security DMZ to edge GPUs — bringing the next horizon of optimization and closed-loop automation directly to the asset.

Physics-Grounded Digital Twins

High-fidelity physics simulation — thermodynamics, fluid dynamics, and mechanical behaviour — grounds every agent in how the asset really works, not just how it was labelled.

Results

Proven at compressor stations

Deployed random-forest and gradient-boosting fuel-optimization models across 30+ North American compressor sites delivered 3–5% savings — then drifted within weeks. Adaptive DRL per component holds the gains and goes further.

Surge is a sudden flow reversal that strikes when a compressor's throughput falls too low for the pressure it is developing — flow breaks down, reverses, and oscillates several times a second, stressing and rapidly damaging the machine (the looping open-loop trace above). The surge line marks that stability limit on the compressor map, and the surge margin is the safety gap conventional anti-surge control keeps to the right of it, opening a recycle valve whenever the operating point drifts too close. OmniPath's adaptive DRL controller continuously adapts to the live gas, temperature, and pressure conditions and anticipates fast transients, so it holds the operating point at a tighter but still-safe margin — recovering the efficiency a fixed margin gives up, without ever crossing into surge.

See the full savings breakdown & compressor-station results →

Architecture

Cloud-to-edge deployment stack

Models train in the cloud, pass through a security DMZ, and run inference on ruggedized GPUs at the asset site — architecture aligned with OT/ICS security standards.

Cloud / AWS

Model training (RL/ML pipelines)
MLOps registry & CI/CD
Secure artifact store (S3/ECR)

DMZ

Validation & security gateway
Compliance & monitoring checks
Air-gapped staging for edge-ready models
Novel signed-boot model verification

Edge

Inference at the asset site on NVIDIA Jetson Orin edge GPU
ConnectTech Anvil RX — intrinsically safe GPU DCS deployment
Local control loop + digital twin integration

Resilient

Ruggedized Orin hardware built for harsh industrial environments.

Secure & Compliant

DMZ ensures one-way, auditable, signed-boot model deployment — aligned with OT/ICS security standards.

Scalable

Cloud-to-edge MLOps enables model updates without downtime.

Use cases

Where OmniPath applies

Oil & gas infrastructure — compressor stations, pipelines, and refinery subcomponents.
Industrial manufacturing — predictive simulation of machines and conveyors.
Energy grid optimization — coordination of distributed energy and demand.
Construction & infrastructure — optimizing site dynamics and flow.
Simulation-as-a-service — adaptive RL + Omniverse without full in-house deployment.

Unlike legacy digital twins (static models, costly manual maintenance) and generic ML platforms (limited physics, no control actions), OmniPath delivers automated builds with physics + DRL out of the box — adaptive, edge-ready, and open-source hybrid.

Pilot program

From baseline to closed-loop in four months

A structured pilot: install a sealed edge GPU unit, collect baseline data, train in the cloud on simulation and historical data, deploy in shadow mode, then activate closed-loop optimization — with weekly KPI reporting and a quantified ROI report at wrap-up.

Pilot options and pricing are provided when you request a pilot.