Packing & cutting

Less waste, from rules that are proven to be better.

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

Packing and cutting software lives on its placement rules. A slightly better rule saves material on every order, which is exactly why a gain that only looks better is expensive.

What we work on

Placement and packing rules
How cutting patterns are generated
The search that combines them

What “better” means

Material waste or fill rate
Number of bins, containers or sheets
Time to produce a result

Proof so far

Google An AI-found rule for packing jobs onto machines in Google’s data centres continuously recovers 0.7% of all compute. Source
FunSearch (DeepMind, 2023) AI-written bin-packing rules beat the classic first-fit and best-fit rules, and kept winning on data they had never seen. Source

Third-party results. They show how much a small rule change is worth at scale.

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 → Exploring

Solver engines

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

See the use case → Exploring

Energy planning

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

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 →