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.
Get In TouchHeavy-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.
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.
Fleet-level models miss asset- and site-specific variability. Constant manual retraining makes the ROI unappealing for most industrial operators.
ESG pressure, rising energy costs, and carbon pricing demand continuous efficiency gains and lower emissions — not one-time tuning projects.
OmniPath integrates three capabilities into one platform — the outcome is assets that are continuously re-optimized as conditions change, inside authority limits you set.
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.
Built on NVIDIA Omniverse and the OpenUSD standard: immersive, multi-user environments for engineering, analysis, and decision-making driven by live or simulated data.
Signed models delivered through a security DMZ to edge GPUs, so the optimizer evaluates thousands of candidate operating trajectories per decision — locally, at the asset, with no cloud dependency in the control path.
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.
Anti-surge and governor controllers do their job well: they protect and regulate individual machines, fast. Load-sharing balances the units that happen to be online. But nothing in the conventional stack decides how many units should be running, which ones, at what split, or what station discharge pressure minimizes total fuel under today's ambient and tomorrow's nominations. Those decisions are made by operators, by rule of thumb, against yesterday's conditions. That's the layer OmniPath occupies.
| Lever | What it decides | Why the existing stack leaves it | Typical contribution |
|---|---|---|---|
| Unit sequencing | How many units online, and which — machines are not identical, with different maps, fouling states, and driver efficiencies | Load-sharing balances units already online; staging is an operator judgement call | Largest single lever on a 3–5 unit station |
| Non-equal load split | Allocates flow to minimize total station fuel rather than to equalize a distance-to-surge proxy | The regulatory layer has no economic objective function | 2–5% |
| Speed vs. recycle | Finds the operating point that meets discharge pressure without opening recycle | No controller in the stack prices recycled gas | Directly attacks the recycle KPI |
| Ambient and composition re-optimization | Re-solves as driver output and compressor maps shift with ambient temperature and gas composition | This is precisely the drift that degrades static models | Continuous, seasonal |
| Anticipatory staging | Starts and stops ahead of a nomination change or upstream swing rather than reacting to it | Nobody owns the forecast-to-setpoint path today | Avoids the worst transients entirely |
This is why the compute lives at the edge on a GPU, and it isn't about control frequency. A control loop with a real valve in it has about a second of dead time; running a policy at ten milliseconds buys nothing and wears out packing. What a GPU buys is depth of search per decision: at every supervisory cycle, OmniPath evaluates thousands of candidate staging and load-split trajectories against an ensemble of learned unit models under ambient and demand uncertainty, and returns the one with the lowest expected fuel cost and the lowest probability of approaching a constraint. That is a planning problem, and it is the one thing a DCS controller structurally cannot do.
Random-forest and gradient-boosting fuel-optimization models deployed across 30+ North American compressor sites delivered 3–5% savings — then drifted within weeks. Adaptive DRL per component holds the gains and goes further: 6–9% station fuel reduction from supervisory optimization across multiple units.
See the full savings breakdown, the assumption set, and why recycle is the cost →
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.
Every stage has an exit criterion, and no stage begins until the previous one has met it. There is no scheduled date on which OmniPath starts writing to your process — there is a standard you set, and we meet it or we don't advance.
Let's bring next-generation adaptive intelligence to your real-world systems.