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Curtailment forecasts. Live now.
A forward curtailment posterior for a connection under Active Network Management or technical-limit regimes, plus the attribution that makes it priceable.
Loom Light forecasts grid risk related to congestion so you can price it correctly for your asset.
Talk to usTo understand it, we have to model the queue, the weather, the demand, reinforcement scenarios probabilistically. Plus adjust the number based on daily changes.
We fit the network model to measured flows, and report which parameters the data cannot resolve.
Queue, weather, demand, dispatch, reinforcement timing, network state. Sampled jointly, not perturbed around one case.
The spread is decomposed back onto its causes. That tells you which uncertainty is worth paying to remove.
A forward curtailment posterior for a connection under Active Network Management or technical-limit regimes, plus the attribution that makes it priceable.
Leonie started her career solving the Schrödinger equation so accurately that computations were often too large for classical computers. That fascination with hard computational problems took her from a PhD in quantum chemistry to the editorial desk at Nature, where she handled research across the physical sciences as Senior Editor. She then moved into deep tech product leadership: first as CPO at quantum computing startup Riverlane, then as VP Product at nPlan, where she built probabilistic intelligence products for infrastructure, forecasting outcomes on some of the world's largest construction and energy projects. That experience taught her what it takes to turn rigorous mathematics into tools that real engineers trust and buy. She founded Loom Light because the electricity grid deserves the same.
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Deepanshu has spent his career proving what computers fundamentally can and cannot do. After studying mathematics at IIT Bombay, he completed a PhD in computational complexity theory at the University of Toronto, where he established new fundamental limits on algorithms for core graph problems. He is now a postdoctoral research associate in Computer Science at the University of Cambridge, working on algebraic aspects of computation. His research sits at the frontier of theoretical computer science, combinatorics, and their connections to other areas of mathematics. At Loom Light, he's channelling that rigour into a different kind of network problem: bringing provably sound mathematical methods to power grid modelling, where the gap between what models assume and what physics demands has real consequences.
Design and data partnerships welcome.
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