Deploy cloud-grade AI everywhere
MountAIn Core is the shared technical foundation beneath Ready, Onboard and IBEX. It brings the trained model, target constraints and surrounding application into one deployment path.
A trained model still has to work inside a real application.
Inference is one part of the deployment problem. Sensor input, pre-processing, model execution, post-processing and data movement all have to fit within the target’s memory, compute and power limits. MountAIn connects model optimisation with the surrounding application path, working with the hardware and backend toolchain already selected.
One SDK.
Three developer roles
MountAIn SDK brings together the tools for application devlopment, mode optimisation and on-device execution within MountAIn Core.
Workflow
Bring a trained model into an application workflow. Define the target and the processing around the model, and make deployment constraints visible early.
Optimisation & compilation
Prepare the trained model for the selected hardware. Optimise the model graph against target constraints before passing it to the hardware backend compiler.
Runtime
Connect model execution to the application on device. Coordinate data movement, processing stages and application behaviour within the existing hardware and software stack.
One foundation,
three product routes
MountAIn Ready
Helps hardware ecosystems make model-to-target evaluation clearer.
MountAIn Onboard
Helps product teams bring AI into hardware and software already in place.
IBEX by MountAIn
Applies the same foundation to autonomous vision in remote and infrastructure environments.
Product conditions
shape every decision.
Optimise for what the complete application can actually hold and move.
Treat energy as a system constraint, not an afterthought.
Keep decisions close to the data when response time or connectivity matters.
Work with the target and its backend tools rather than forcing platform change.
Create a route engineering teams can test, understand and carry forward.
Two developer starting points
Python and model-first teams
Begin with the model, framework and success condition. See what changes when the target becomes real.
Embedded and product-first teams
Begin with the board, device stack and operating constraints. Bring intelligence into the architecture without discarding the engineering already done.
What good evidence looks like
A useful result identifies the model, task, input, target, precision, toolchain and test conditions. It shows what changed, what remains in the product stack and what has not yet been proven.
- Python and model-first teams begin with the model and success condition.
- Embedded and product-first teams begin with the device stack and operating constraints.
