The startup provides an AI-driven platform that ingests SCADA, weather, and market data to generate sub‑hourly demand forecasts for utilities and grid operators. Its continuously retraining models feed a constraint‑aware optimization engine that delivers real‑time dispatch set‑points for generators, storage, and demand‑response resources via API and web console, helping reduce reserve margins and improve grid reliability.
Funding
Funding not disclosed
Founders
Product
Problem
Utilities face increasing volatility in electricity demand and intermittent renewable generation, which makes real‑time balancing of supply and load complex and costly. Traditional forecasting tools often lack the granularity and speed needed to prevent over‑generation, curtailment, or costly peak‑shaving measures.
Solution
The company offers an AI‑driven platform that ingests SCADA, weather, and market data to produce high‑resolution demand forecasts for transmission and distribution operators. Its machine‑learning models continuously retrain on incoming telemetry, enabling near‑real‑time adjustments to generation dispatch and storage utilization. An automated optimization engine translates forecasts into actionable set‑points for distributed energy resources, demand‑response programs, and conventional generators. The platform delivers these recommendations through a secure API and a web‑based operations console, allowing utilities to reduce reserve margins, lower fuel consumption, and improve overall grid reliability.
Target Audience
The primary customers are electric utilities, transmission system operators, and large‑scale renewable aggregators that manage grid balancing and demand‑response programs. Secondary users include independent system operators and energy service companies seeking data‑driven dispatch optimization.
Features
- Multi‑source data ingestion pipeline that normalizes SCADA, IoT sensor, weather, and market feeds in real time
- Gradient‑boosted and deep‑learning demand‑prediction models with sub‑hourly resolution and confidence intervals
- Constraint‑aware optimization engine that solves mixed‑integer linear programs for generation scheduling and storage dispatch
- RESTful API and WebSocket endpoints for seamless integration with existing Energy Management Systems (EMS) and SCADA platforms
- Interactive dashboard with heat‑maps, forecast variance visualizations, and automated alerting for forecast deviations
- Role‑based access control and end‑to‑end encryption to meet NERC CIP and ISO 27001 security standards
- Automated model monitoring and drift detection to trigger retraining without manual intervention