Ecosystem
Member of the NVIDIA Inception Program
Heterodyne makes modern electricity networks observable and predictable, accelerating the path to a decarbonised, stable and autonomous grid. In high-DER, low-inertia systems, we combine predictive physics with graph neural network surrogates to de-risk the integration of distributed energy resources.
Reduces state-estimation uncertainty across unmonitored distribution networks.
Forecasts vegetation encroachment and systemic cascades with graph neural networks.
Replicates the LV network in physics to simulate voltage and losses in real time.
Every product is anchored to network physics and validated against full physical simulation, with machine learning applied only where it adds speed and coverage.
Predicts, quantifies and helps prevent cascading failures in DER dominated networks. Fuses live telemetry with physics-informed stochastic models and a graph neural network cascade surrogate, resolved into a single real-time reliability index.
Explore →A GNN-guided robotic sensor fleet that learns where to measure next, converting network blind spots into high-confidence state estimates within a fixed fleet budget.
Explore →Fuses hyperspectral satellite imagery to forecast vegetation encroachment and wildfire-ignition risk along grid corridors, up to 90 days ahead, at near-zero marginal cost.
Explore →A distribution-grid digital twin: a physics-grounded replica of the MV/LV network that solves a full power flow for every scenario.
Explore →“Our mission is to build the cognitive layer for the world’s most critical infrastructure”
Talk to us about deploying grid intelligence on your network
Consult us →Our mission