Pravāh builds machine-learning and physics-based software for electric power systems. Its technology helps utilities, data-center developers, hyperscalers, chip companies and power-market participants model grid conditions, forecast demand, generation, weather and electricity prices, automate electrical studies, assess reliability and congestion, and identify how existing infrastructure can support more load. The company works with utilities serving tens of millions of consumers across India, Germany and the United States. Its public research includes models and benchmarks for power-flow optimization, distribution grids, weather forecasting and graph neural networks.
Pravāh offers candidates the opportunity to work on technically difficult problems at the intersection of machine learning, power systems, optimization, weather and large-scale infrastructure. The company reports $7M in funding from Khosla Ventures, Pear VC and Conviction and works with utilities serving tens of millions of consumers across multiple countries. Its team publishes research and open-source work, while products such as Indus and Pachira put forecasting models into operational use. Current engineering roles involve utility-scale geospatial and time-series data and offer significant architectural ownership; some published roles also include equity. As an early-stage company, responsibilities are likely to be broad and the operating environment fast-moving.
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Database
Cloud
Infrastructure
Data Visualization
Co-founder and CEO of Pravāh. Mangal has an MBA from Stanford Graduate School of Business and an MA in International and Development Economics from Yale. Before Pravāh, he worked as an economist with the World Bank and J-PAL. He co-founded Pravāh to apply machine learning to grid planning and operations.
Co-founder and CTO of Pravāh and an energy scientist and machine-learning engineer. Suri earned a PhD in Energy Science & Engineering and an MS in Energy Resources Engineering from Stanford, where he was a Knight-Hennessy Scholar. His work focuses on machine learning, optimization and modeling for electric power systems.
Founding machine-learning engineer at Pravāh. Hilmarsson studied at Stanford and previously led AI initiatives at Zoom spanning graph machine learning and generative-AI products. At Pravāh he contributes to machine-learning research and engineering for grid and weather models.
Chief Strategy Officer at Pravāh. Sykes works on strategy around electricity markets and energy systems and has published analysis on topics including India's electricity market, demand flexibility and energy transition. He is part of Pravāh's current leadership team.
Pravah on LinkedIn
Pravāh detailed its work on Pachira, a price-forecasting model for India's electricity market. The system forecasts Indian Energy Exchange day-ahead prices at multiple horizons and is intended to support electricity-market decision-making.
Read articlePravah on LinkedIn
Pravāh launched Indus-wx, a weather-forecasting system designed for India's electric grid. It combines satellite, ground-station and numerical-weather data and produces frequently refreshed high-resolution forecasts for variables relevant to renewable generation.
Read articleThe Times of India
Andhra Pradesh Eastern Power Distribution Company approved a pilot with Pravāh to use AI-driven grid intelligence and load-flow analysis to identify electricity losses. The project was reported as being funded under the Ministry of Power's Powerton program.
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