Guardrails we put on every production AI system
Cost controls, evaluation loops, and fallback behavior we treat as non-negotiable.
SD
Soumyadeep Dey·Apr 3, 2026·9 min read
A demo that works 80% of the time is exciting. A production system that fails 20% of the time is a liability. The gap between the two is entirely guardrails.
Evaluation before launch
We never ship an AI feature without a test set of real inputs and expected behavior. It turns "it feels better" into a number we can actually defend to a client.
Controls that stay on in production
- Cost ceilings per request and per tenant, enforced in code
- Fallback behavior for when the model is slow, wrong, or down
- Observability on every call: inputs, outputs, latency, and cost
The model is the easy part. Everything that keeps it safe, fast, and affordable is the actual product.
Have something like this to build?
Tell us the goal, the constraints, and who signs off. We scope it against real delivery capacity and ship the first build in weeks.