Gigaton

Gigaton sells a Self-learning Control system for energy-intensive industrial plants. The system forecasts process variables such as temperature and gas composition, retrains its models over time, and connects directly to the plant's DCS to execute control actions. It also shows the reasoning behind each control decision and provides a digital twin of the plant for testing controllers in simulation.

The website names cement, steel, glass and mining plants as target customers. A case study describes Heidelberg Materials deploying the system, citing a 4% reduction in fuel cost index, a 33% reduction in C3S variability, a 2% reduction in fuel-derived carbon emissions and 8 weeks to go live with a plant. The website does not describe pricing or the commercial model.

Gigaton's about page says the company was founded by a team that includes physicists and machine learning engineers, but it does not state a founding year. The team page lists a CEO, CTO, COO and several co-founders, and the company reports investors including Albion VC, Clean Growth Fund, Planet A, 2150 VC and Semapa Next. The company has seven open roles, all based in London, covering engineering, machine learning, solutions and deployment, sales and operations.

Product: Self-learning control system that forecasts process variables, runs a plant digital twin and connects to the DCS to control kilns and production lines.