The grid is at its limits.
We are building the AI Engine to transform it.
Pravāh maps and understands the grid in real time, helping utilities manage demand, generation, and congestion with confidence.
The world is changing.
The grid is not.
Forecasting is getting harder
Extreme weather, EVs, and rooftop solar are making demand and generation harder to predict.
Utilities lack visibility
Outdated asset records create blind spots at the feeder and transformer level.
Grid modelling is unreliable
Utilities operate with partial and noisy data, leading to risky operations.
The future cannot be tested
Operators cannot explore enough possible futures to spot risks before they impact customers.
We use machine learning to give utilities a real-time understanding
of how the grid behaves under stress.
Deep Learning-based Forecasting
Model electricity demand and distributed generation across time horizons, capturing short-term volatility and long-term patterns that legacy methods miss.
Grid Modelling Using Graph Neural Networks
Move beyond static grid models by learning how conditions propagate across the network, even when measurements are noisy or incomplete.
Mapping Grid Infrastructure Using Computer Vision
Use computer vision on satellite and street-level imagery to map grid assets and rooftop solar, revealing blind spots in the distribution network.
Probabilistic Grid Simulations using Reinforcement Learning
Test thousands of possible grid futures to identify risk and failure modes before they occur.
Deployed with utilities across three continents.
Used by electric utilities across India, Germany, and the United States to forecast demand, model grid constraints, and reduce operational risk in live systems.
Research & Publications
Assessing global machine learning weather prediction models during extreme events
PowerGNN: a topology-aware graph neural network for electricity grids
