Beyond optimisation
Faster code, where speed is the product.
For teams whose code has a speed or size objective: compilers, databases, ML training.
The same approach works for any algorithm with a number that says what better means. We have not run Argmax on these yet. If this is your code, talk to us about being first.
- Compiler passes
- Database query planners
- GPU kernels and ML training code
- Hot paths in large applications
- Run time or latency
- Throughput
- Binary or memory size
- Google A 23% faster matrix kernel made Gemini’s whole training run 1% faster. Source
- JetBrains A 15–20% win on a test benchmark was about 4.6% in the real IDE, and only 2 of 5 changes held up. That gap is why proof matters. Source
Third-party results with Google’s AlphaEvolve. Nothing on this page is an Argmax result yet.
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.
Other use cases
Supply-chain planning
For companies that build planning software: forecasting, inventory, production planning.
See the use case → Open for a pilotMachine & job-shop scheduling
For companies that build scheduling software for factories: jobs on machines.
See the use case → Open for a pilotPacking & cutting
For companies whose software packs or cuts: bins, containers, pallets, sheets, rolls.
See the use case → ExploringSolver engines
For companies with a MIP, SAT or constraint solver inside their product.
See the use case → ExploringEnergy planning
For companies that build planning software for power plants, batteries and EV charging.
See the use case →