AI that keeps the lights on and the drill bit honest
Physics-informed models that find geothermal reservoirs before you drill and read the grid before it strains — built on decades of seismic and load data, with uncertainty quantified on every call. Exploration cost cut 60%.
A Hominis app module · your energy data, app-ified
From the reservoir to the grid edge
Physics-informed models that find the resource before the drill and read net-load before it swings.
Subsurface reservoir detection
Predict the location and characteristics of geothermal and subsurface resources from existing seismic surveys, geological maps and geochemical data — before committing to a drilling campaign.
85% detection accuracy
Drill-risk & exploration cost reduction
Turn uncertain, capital-heavy drilling campaigns into risk-ranked decisions. Survey-to-prospect time collapses and the dry-hole rate drops because every prospect ships with a confidence interval.
60% lower cost · 18 months to 3
Grid load & duck-curve management
The DuckCurve instrument reads net-load shape in real time — anticipating the steep evening ramp and midday solar over-supply so dispatch and storage stay ahead of the swing instead of chasing it.
Real-time net-load forecasting
Renewable generation forecasting
Wind and solar output predicted across horizons so balancing, curtailment and storage decisions are made on what generation will actually do, not on yesterday's average.
Horizon-aware output forecasts
Asset reliability & predictive maintenance
Anomaly detection across plant, field and substation telemetry catches degradation early — faults surfaced before they become unplanned outages on critical infrastructure.
Faults caught before downtime
Trusted by the people who bet the drilling budget
The two design choices that made exploration teams and grid operators act on the model.
Physics-informed models geologists trust
A 3D convolutional architecture for volumetric seismic interpretation, constrained by geological priors and thermodynamic loss functions. Predictions obey the physics of the subsurface — which is what earned the trust of teams who have to bet a drilling budget on the output.
Uncertainty quantified on every prediction
A Bayesian layer attaches a confidence interval to every reservoir and load forecast, enabling risk-aware drilling and dispatch. In capital-intensive, safety-critical operations, a calibrated “how sure are we” is what made the model deployable — not raw accuracy alone.
One method, tuned for energy
Assess, Transform, Sustain — the cycle every organization runs, dropped one level deeper for your sector's pains and sticky aspects.
Assess
Seismic + load-data audit and drill/dispatch opportunity ranking
We inventory the heterogeneous reality of energy data — decades of seismic surveys collected with different methods, sparse well logs, SCADA and smart-meter load histories — and assess where physics-informed AI changes a real decision. The output is a ranked roadmap of exploration and grid use cases scored by capital at risk and confidence achievable, in 4–6 weeks.
Transform
3D physics-informed models from pilot to risk-ranked production
We build the volumetric seismic and net-load models with geological priors, thermodynamic constraints and a Bayesian uncertainty layer baked in — then harden them into production where exploration teams and grid operators act on confidence intervals, not point estimates. Prospects ship validated against known fields; the DuckCurve instrument ships wired into dispatch.
Sustain (AIOps)
Recalibration against drilling outcomes and seasonal grid drift
Energy models decay against ground truth: every drilled prospect confirms or corrects the reservoir model, and load patterns drift with seasons, EV adoption and new renewables. Sustain (AIOps) closes that loop — retraining on confirmed results, recalibrating duck-curve forecasts as the generation mix shifts, and monitoring uncertainty bands so a model stays honest across new basins and a changing grid.
Geothermal reservoirs, found before the drill bit
Deep learning on seismic and geological data — 85% detection accuracy, exploration costs down 60%.