Controls that keep AI behavior safe and predictable: input validation, policy checks, constrained outputs, fallbacks, and human review where needed.

AI-native mobile • measurable quality

Evaluation & guardrails — so AI is defensible in production.

Q: What does evaluation look like in practice?

A small, representative test set, target metrics, failure-mode checklist, and instrumentation to monitor quality in pilot and production.

Q: How do you handle failures?

We design explicit fallbacks (rules, templates, manual steps) and define stop conditions when the model cannot meet acceptance criteria.

If procurement, security, or operations can’t trust it, it won’t ship. We build AI features with acceptance criteria, test sets, fallbacks, and instrumentation — before the expensive code lands.

Acceptance criteriaTargets for accuracy, latency, and failure rates

GuardrailsPolicy checks, constrained outputs, human review where needed

TelemetryQuality monitoring in pilot + production

Request a scope checklist Email dp@sternika.com

Guardrails we commonly use

Input validation + redaction for sensitive fields
Constrained outputs (schemas, allowed actions)
Policy checks (business rules + safety filters)
Fallbacks: rules/templates/manual steps

What we instrument

Latency per device and per workflow step
Success vs fallback rates
Human corrections / overrides
Crashes and session-level diagnostics

FAQ

Short answers — no slide-ware.

What does evaluation look like in practice?

Test set, metrics, failure modes, and monitoring — tied to acceptance criteria.

What are guardrails?

Controls that keep behavior predictable: constraints, policy checks, and human review.

How do you handle failures?

Explicit fallbacks and stop conditions. Reliability wins over “smart-looking” outputs.

Can this be on-device?

Yes — when privacy/latency/offline constraints require it.