Moving from AI Experiments to Production

Why most AI experiments stall

Pilots demonstrate what is possible, but production demands reliability, cost control, safety and observability. An assistant that works on a demo dataset can fail silently on real data.

What production-grade AI requires

• Grounding: retrieve from your own knowledge so answers are accurate and citable.
• Guardrails: validate inputs and outputs, and keep humans in the loop for high-stakes actions.
• Evals: measure quality continuously, not just at launch.
• Observability: trace every request, token and tool call.
• Cost control: cache, route and budget so AI scales economically.

A practical path forward

Start with one well-scoped workflow, instrument it heavily, and expand once it is reliable. AI that is small, grounded and observable creates far more value than a broad but brittle rollout.