Stable, an EV charging data analysis platform, has now used its proprietary prediction software to analyze 70 million data points for prospective charging site locations across the US, and has also completed its Series A funding round.

Lead investor Congruent Ventures is joined by Homecoming Capital and Ironspring Ventures this round, along with seed investors Trucks Venture Capital, Ubiquity Ventures, E14 Fund, Ahoy Capital, Upside Partnership, Qasar Younis and others–bringing total funds raised to $14 million.

Stable co-founders and MIT Media Lab alumni, Rohan Puri and Jamie Schiel, doubled its funding to accelerate hiring of data scientists and engineers to help guide the next $39 billion in EV charging deployments and spending from the Bipartisan Infrastructure Law (according to calculations from Atlas Policy).

EV charging stations profitablity remains critical to adoption, as Stable’s own data shows that most chargers are used less than 10% of the time, and very few are used more than 20% of the time.

Stable’s Machine Learning models help financial institutions, infrastructure developers and charging networks turn arbitrary decisions into data-driven ones based on a variety of factors like average trip distance, nearby amenities and energy costs. Predicting charger usage is just the beginning of EV adoption, and we’ll continue to help our customers deploy, track and monitor their investments.

—Rohan Puri, Stable co-founder and CEO

We have been closely studying the challenges to EV infrastructure deployment, as millions of chargers are coming online over the next few years, and Stable is solving a major pain point. Their highly accurate Machine Learning analytics—truly unique in the industry—allow customers to optimize their investments by placing public EV chargers in the best locations, which is essential to ensure consistent profitability for owners and smooth journeys for EV drivers.

—Jackie Kossmann, partner at Congruent Ventures