Production-readiness
Identify where an AI or agentic application lacks boundaries, evidence, recovery paths or operating assumptions.
From prototype to dependable system
You have a promising AI app.Now it needs a production path.
The hard part is often no longer getting a model to respond. It is giving the system safe authority, useful evidence, predictable operations and a clear way to recover when things go wrong.
What we can clarify together
Build the system around the model.
I work from the point where a useful demo meets the parts that need engineering judgement: architecture, policy, platform capability and operational control.
Platform and delivery
Shape the infrastructure, deployment path, access model and developer workflow around reliable change.
Authority and policy
Separate what a model may propose from what the platform may allow, request or deny.
Observability and verification
Make behaviour inspectable through traces, outcomes, evaluation and operational signals.
Reliability and cost
Design for capacity, failure, recovery, safe automation and the costs that arrive after launch.
A useful starting point
Start with the decision that needs to be safe.
Then work backwards: what evidence is needed, what capability should exist, what policy governs it and how do we know the outcome was right?
Discuss your situation →