AONG Technologies exists to close the distance between a promising scientific model and a system a research group can run, trust, and maintain.
Mission
Make numerical rigour the default
Scientific ML fails quietly: a gradient that leaks, a kernel that strides badly, a BF16 rollout that drifts. We build tools that surface these failures before they cost GPU-weeks or a retracted result.
Approach
Software engineering standards for research code
Typed interfaces, reproducible builds, regression guards on numerics, and review processes that a new lab member can follow on day one.
Focus
Four bottlenecks, not forty features
CUDA and Triton kernels, physics-informed neural operators, numerical stability analysis, and CI benchmarking. We stay deep in these rather than broad across everything.
Model
Platform plus engineering support
Every plan above Free includes access to engineers who have shipped scientific computing code in production, not only documentation.