Core question
Can this be trusted in operation?
The question is not only whether the AI output looks good. It is whether the workflow, data boundary, failure handling, human control and operational ownership are clear enough for real use.
AI hardening
Make AI work safer to rely on before people depend on it.
AI hardening is the discipline of turning AI-enabled ambition into something that can be evaluated, governed, supported and owned. It sits between experimentation and production dependency.
Review areas
Evaluation, controls and ownership
I look at prompts, model usage, data exposure, human-in-the-loop design, integration points, auditability, failure modes, cost behaviour, monitoring and decision responsibility.
Fit
For products and internal workflows
This applies to AI-coded products, LLM-enabled features, agentic workflows, operational assistants and AI components embedded into customer or employee journeys.
Outcome
A practical hardening backlog
The output should be a clear view of what can proceed, what must be hardened first, what should be constrained and what should not yet be put in front of users.
Common questions before we talk
A few practical answers to help you decide whether this is the right conversation.
What is AI hardening?
AI hardening is the work required to make an AI-enabled product, workflow or system reliable, controlled, observable and safe enough for production use.
What should be checked before an AI feature goes live?
Key checks include data handling, model behaviour, evaluation approach, human control, failure modes, monitoring, integration safety, cost exposure, security and ownership.
Does AI hardening slow teams down?
Done well, it reduces waste. It helps teams move faster on what is safe to scale and avoid expensive rework where assumptions are weak.
Next step
Discuss the situation.
Share the context, pressure and decision in front of you. I will respond where a serious advisory conversation makes sense.