The model works. The system does not.
A successful notebook or prototype reaches production without repeatable deployment, rollback, ownership, or service-level behavior.
06 / AI platform engineering
Give AI teams a production path, not a GPU-shaped exception.
We extend the platform with self-service paths for model delivery, inference, accelerators, data access, observability, and policy so AI can operate like production software.
Put AI on the platformENGAGEMENT.SCOPEACTIVE
01Model and inference delivery
02Accelerator scheduling
03AI golden paths
04Observability and governance
Failure modes
AI does not need a second infrastructure religion.
Accelerators are scarce and invisible
GPU capacity is requested through side channels, poorly attributed, and disconnected from workload priority or unit cost.
Every AI workload invents operations again
Model artifacts, secrets, data access, routing, telemetry, and safety controls become custom work for every team.
What gets built
Working systems. Visible evidence. No ceremonial roadmap.
+
AI workload and service templates
+
Model promotion and rollout pipelines
+
GPU and accelerator capacity controls
+
Inference routing and autoscaling
+
Token, latency, quality, and cost telemetry
+
Policy, identity, and data-access guardrails
Operating sequence
Move before the deck gets stale.
- 01
Follow one model to production
We trace artifacts, data, runtime, capacity, routing, security, and operational ownership from experiment to serving traffic.
- 02
Ship one AI golden path
A real workload proves repeatable deployment, observability, rollback, and accelerator use on the platform your teams already operate.
- 03
Turn AI into a platform capability
Teams consume supported patterns instead of building isolated infrastructure around every new model, agent, or inference service.
Start with the failure mode
Show us where delivery gets ugly.
One call. Senior engineers. No discovery theater. We will tell you what we see, what we would attack first, and whether we are the right crew to do it.