Ongoing evaluations
Run representative test cases as the system and its inputs change.
All services
DESIGNED FOR REAL WORKFLOWS / 04Explore the service
BUBBLES AI / SERVICE 04
AI Systems Reliability.
AI systems need checks after launch. We help teams evaluate outputs, monitor failures and respond when changing data or model behaviour affects the experience people rely on.
Discuss AI reliabilityDESIGNED FOR REAL WORKFLOWS / 0401 / THE REASON
Why this matters
A launch test covers only the cases known at launch. New inputs, integrations and model updates can change results over time, so teams need measures that reveal problems and a process for acting on them.
02 / THE WORK
What we put in place
Output checks
Flag responses that need review or fail agreed quality and safety criteria.
Monitoring
Track errors and quality signals so the team can investigate changes in behaviour.
Incident response
Agree owners, escalation paths and rollback options for problems that reach production.
03 / THE OUTCOME
A measurable operating process for detecting issues, reviewing changes and keeping the system useful after launch.
04 / QUESTIONS
Common questions
Can you assess a system another team built?
Yes. We can start with an evaluation of its access, architecture and current behaviour, then recommend a monitoring scope.
What happens when a check fails?
The agreed response plan identifies who reviews the issue, how it is escalated and whether the system should be paused or rolled back.