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Energy planning

Cheaper plans for power, storage and charging.

For companies that build planning software for power plants, batteries and EV charging.

Energy planning software decides which plants run, when batteries charge, and how to bid. The rules inside it set the cost of every plan. We have not run Argmax on energy code yet. If this is your code, talk to us about being first.

What we work on
  • Unit-commitment heuristics: which plants run, and when
  • Charge and discharge rules for batteries and EV charging
  • Bidding rules for energy markets
What “better” means
  • Operating cost
  • Profit from storage or charging
  • Time to produce a plan
Proof so far
  • FunSearch for unit commitment (research, 2025) An AI-written heuristic cut operating cost by 6.7% against a genetic algorithm, on a 10-plant, 24-hour test system. Source
  • University of Adelaide (research, 2026) AI-evolved EV charging rules earned 118% of the profit of an expert-written rule, on real Australian price data. Source

Research results on small test systems, not Argmax results. They show the approach works on energy rules; production-size proof is still to come.

The checks are the same everywhere

Old and new code run on the same problems, several times each. A statistical test rules out luck and nothing else may get worse. Some problems stay out of the AI’s reach; results on those are measured and reported too. Every accepted change arrives as a pull request your engineers review.

Book a call → How it works

Other use cases

Open for a pilot

Supply-chain planning

For companies that build planning software: forecasting, inventory, production planning.

See the use case → Open for a pilot

Machine & job-shop scheduling

For companies that build scheduling software for factories: jobs on machines.

See the use case → Open for a pilot

Packing & cutting

For companies whose software packs or cuts: bins, containers, pallets, sheets, rolls.

See the use case → Exploring

Solver engines

For companies with a MIP, SAT or constraint solver inside their product.

See the use case → Exploring

Beyond optimisation

For teams whose code has a speed or size objective: compilers, databases, ML training.

See the use case →