← All use cases Open for a pilot

Supply-chain planning

Better plans from the planning engine you already have.

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

Your product already turns data into plans. The rules, heuristics and pre-processing steps inside it decide how good those plans are, and how fast they come back. That is the code we improve.

What we work on
  • Forecasting pipelines and the models inside them
  • Inventory and replenishment rules
  • Production-planning heuristics, and the steps that shrink a model before a solver runs
What “better” means
  • Forecast error (for example WMAPE or RMSE)
  • Plan cost or service level
  • Time to produce a plan
Proof so far
  • Kinaxis An AI-improved heuristic that shrinks planning models scored about 2× better than hand tuning, on Kinaxis’s own measure. Tested on one problem so far. Source
  • Kinaxis A forecasting pipeline got 22.6% lower error and took 90% less time to run. Source
  • Coolblue 28-day demand forecasts got more than 5% more accurate, after about 200 tries. Source

These are other companies using Google’s AlphaEvolve. They show that this kind of code has room to improve. Argmax adds the part they had to do themselves: proving each gain is real.

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

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

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 →