One correlation layer.
Five ways in.
The vertical trace is the platform. These are the first products it powers. We lead with the two highest-conviction bets and let the evidence rank the rest — conviction here means where the signal points today, not a shipping promise.
Utilization Truth ★
Prove the gap between reported utilization and achieved compute — continuously, on real clusters. The wedge the whole company is named after.
for ML platform & infra teams
Kernel Profiler ★
Per-kernel achieved-vs-peak for torch.compile / Triton, right in your dev loop. A tool developers adopt one at a time — the beachhead.
for kernel & model engineers
Inference Tail-Latency
Attribute p99 latency spikes to the specific kernel, batch shape, or memory stall that caused them — not just "the GPU was busy."
for inference / serving teams
Straggler Attribution
In multi-node training, one slow rank stalls the whole all-reduce. Find the exact rank, link, or kernel dragging the collective.
for large-scale training teams
GPU FinOps
Turn wasted GPU-hours into dollars by joining achieved work to the cloud bill. Compelling, but downstream of the measurement layer.
for eng leadership & finance
Everything on this page is either buildable on signals that exist today (DCGM, CUPTI, Nsight) or clearly marked as exploration. We don't publish metrics we haven't measured.