01
Starts from the model
Bring a trained model and defined task rather than a generic score.
MountAIn Ready
Cloud-Grade Computer Vision, on a Microcontroller.
A meta-compiler and embedded orchestrator that slashes memory use by 2.5× and doubles frame rates on low-cost MCUs — automating quantization, pre-compilation, and multi-core hardware mapping in one click.
Make capability easier to prove
Can this model become a credible application on this target?
A model may exceed the practical memory envelope. Surrounding processing may not fit. Toolchain hand-offs add friction. Ready makes those constraints visible before a promising evaluation becomes a specialist engineering project.
What Ready does
Evidence developers and ecosystems can act on.
02
Makes constraints visible
Assess memory, processing stages and the selected hardware path together.
03
Prepares the graph
Optimise before the target compiler while preserving the vendor toolchain.
04
Produces usable evidence
Attach conditions so every team can judge relevance for itself.
For hardware ecosystems
Move from AI support to credible evaluation.
Ready helps silicon and module providers show how a real model maps to the target, where the constraints are and what the developer should do next.
- More credible evaluation journeys
- Reproducible model-to-target evidence
- Better technical conversations with developers and field teams
- A clearer path from interest to design consideration
Trained model
MountAIn Core
Deployed application
How an evaluation works.
- 01
Define
One model, task and success condition.
- 02
Select
Confirm target and toolchain.
- 03
Review
Expose compatibility and constraints.
- 04
Reproduce
Repeat the agreed result.
A practical first step.
One real model on one real target is enough to establish whether there is a useful route forward.