Turn owned GPU clusters into production model systems.
We handle workload acceptance, model production, and operating handoff for enterprises running private AI factories.
Cluster Diagnostic
Cluster accepted; workload behavior unproven.
Production Enablement
Blockers known; foundation not operational.
Workload Delivery
One model path needs production criteria.
Resident Engineering
Multiple teams need utilization and control.
This is not hardware integration. We start after the infrastructure is accepted or close to accepted, and own the path from working machines to working model production.
This is not generic AI consulting. The work is technical and measurable: utilization, step time, MFU, queue health, evaluation quality, serving latency, throughput, and cost per useful output.
One accountable layer from acceptance to operating handoff.
We create the operating layer that sequences workloads, measures performance, sets release gates, and hands the system to the internal team.
Accepted capacity enters with fabric, data, and queue constraints; production leaves with reproducible runs, eval gates, serving targets, and handoff packages.
Built for regulated private clusters.
Our delivery model assumes internal data boundaries, security review, platform ownership, and executives who need measured return from owned infrastructure.
Cluster state, topology evidence, workload probes, and ranked blockers.
Quality gates, regression signals, acceptance thresholds, and release criteria.
Runbooks, dashboards, ownership map, incident path, and extension backlog.
Bring one workload.
Start with the cluster state, the target workload, and the internal owners.