01
Production delivery path
Versioned artifacts, environments, evaluation gates, policy, deployment, and rollback in one control loop.
AI from prototype to production
The demo worked. Production is the hard part.
Turn models, agents, and inference services into governed production systems with repeatable delivery, observable behavior, and cost boundaries.
Productionize the pathThe signal
If nobody can explain the rollback, evaluation gate, or cost per useful outcome, the AI system is not production-ready.
When to call
The system is already telling you.
01
A successful notebook or agent demo has no repeatable deployment path.
02
Teams cannot trace model, prompt, data, configuration, and application versions together.
03
Latency, accelerator use, token spend, and output quality are measured in different systems or not at all.
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Security and governance arrive after architecture decisions have already hardened.
What changes
Evidence your team can operate.
02
Behavioral telemetry
Trace quality, latency, cost, failures, and dependency behavior from request to outcome.
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Governed operations
Access, data boundaries, audit evidence, capacity, and incident ownership designed into the platform.
The engagement
Fast enough to matter.
Senior engineers stay on the work from first signal through operational handoff.
- 01
Interrogate the prototype
Trace dependencies, data, evaluation assumptions, runtime constraints, and failure modes.
- 02
Build the production slice
Ship one end-to-end workload with delivery, telemetry, controls, and explicit rollback.
- 03
Standardize what survived
Turn proven patterns into reusable platform capabilities instead of premature framework.
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.