The important part is not just collecting data. The product is the system that converts noisy runtime traces into ordered, explainable, and testable actions.
We collect the minimum profiler, runtime, and allocator signals needed to understand where your GPU time and memory are actually going.
Instead of dumping raw timelines on your team, we map trace signatures to known bottleneck families and attach a concrete diagnosis.
Recommendations are ordered by expected uplift, implementation effort, confidence, and risk so the first action is obvious.
Every suggested change is meant to be benchmarked, checked for regressions, and fed back into the policy layer over time.
A good optimization system cannot stop at suggestions. It needs guardrails, confidence signals, and a way to prove that the proposed change actually improved the workload.
Live workload traces from training, inference, or simulation jobs
Classifier maps telemetry patterns to bottlenecks and possible fixes
System ranks the next action by ROI, effort, confidence, and risk
Teams get a validated optimization path instead of a messy dashboard
If the workflow makes sense, the next question is simple: what would the system find in your actual GPU workload?