AI presentations are polished by design. Demos run in controlled environments. What cafés need is clarity about real-world behavior, not best-case scenarios.
Ask what happens when the system produces the wrong output. Strong providers explain escalation paths. Weak ones redirect to accuracy metrics.
After launch, responsibility doesn’t disappear. Someone must monitor, adjust, and respond to exceptions. If ownership is unclear, issues linger.
Cafés should ask how systems behave during peak hours. Reliability under pressure matters more than performance during quiet periods.
At Auvexen, evaluation focuses on failure modes, not promises. We prefer clear limits over broad claims because predictability builds trust over time.