Market intelligence and AI analytics for battery, solar and wind assets
Modo Energy sells benchmarks, revenue forecasts and an AI analyst to firms investing in, lending to and trading grid-scale storage and renewables. Its stack pairs a Python data layer (Airflow, Spark, Pandas, scikit-learn) with LangChain for LLM work and a TypeScript/Next.js frontend now consolidating on Turborepo. Accelerating hiring across the UK, US and Australia, split between engineering, data and sales, points to product build-out alongside APAC and US expansion.
Modo Energy, founded in 2019 and headquartered in London, provides market intelligence for battery storage, solar and wind assets. Its products include regulated benchmarks, bankable revenue forecasts, analyst research and Ko, an AI analyst. Customers are investment, trading, lending and development firms working in power markets. The company is privately held, has 51–200 employees, and hires in the United Kingdom, United States and Australia.
Modo Energy uses Python, Django, FastAPI, Apache Airflow and Spark for data, TypeScript, React and Next.js for frontend, AWS with Terraform for infrastructure, LangChain for AI, and HubSpot, Salesforce and Gainsight for go-to-market.
Job posts point to LLM infrastructure for its AI analyst Ko, energy market data pipelines, a high-performance live market data interface, a frontend monorepo, and selling its terminal to APAC investors.
Modo Energy hires in the United Kingdom, the United States and Australia, with roles across engineering, data, sales, support, marketing and product.
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Modo Energy's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.