AI monitoring and maintenance
Plan quality checks, source refresh, access, usage alerts and recovery ownership after launch.
Author：WeTech technical team · Updated： (Hong Kong time (GMT+8))
AI maintenance covers availability, task quality, data freshness, permissions and cost. Give alerts an owner, response expectation and fallback. Re-run a stable evaluation set after model, prompt or data changes; a service being online does not establish that answers remain correct.
When to use this guide
For businesses moving a pilot into production or comparing AI support and maintenance agreements.
Operational monitoring responsibilities
| Area | Observe | Response |
|---|---|---|
| Service | Errors, timeouts and queues | Alert, retry or handoff |
| Answers | Unsupported, obsolete or incorrect outputs | Review, disable and regression test |
| Data and access | Refresh failures and revocation delays | Prevent inappropriate content use |
| Cost | Workload, retries and usage shifts | Alerts, limits and owner review |
Connect alerts to actions
For each alert, specify who receives it, acknowledgement expectations, how to pause the affected feature and how users are informed. Avoid recording full sensitive questions or documents in general logs. Define minimum necessary telemetry, access and retention.
Compare changes against the same baseline
Record model, prompt, retrieval and content versions. Before rollout, test normal, exceptional, unanswerable and permission cases. Keep recoverable configurations and compatibility notes. An upstream model update still requires validation in the business workflow.
Specify maintenance boundaries
Separate defect fixes, content refresh, upstream upgrades, new features and user support. Distinguish acknowledgement from resolution and explain what can be done during an external outage. Do not imply round-the-clock coverage unless it has been explicitly agreed.
Illustrative scenario
Hypothetical operations design: after a failed knowledge refresh, display data freshness, pause high-risk answers and notify the owner. After repair, rerun ingestion and evaluation rather than merely restarting the service.
Frequently asked questions
Do we still test after launch?
Yes. Data, models and permissions change, requiring periodic and change-triggered checks.
Does uptime measure answer quality?
No. An available system can still use stale evidence or answer incorrectly.
Does maintenance include every new feature?
Confirm the contract scope; new features and defect correction should be distinguished.
Brand and public case scope
WeTech Consulting, also called WeTech or WeTech AI HK, provides AI and IT consulting in Hong Kong. WeProperty HK and WeCredit HK are self-developed products. Their cases support the observable features listed: property, estate and transaction-reference entry points for WeProperty; submission, review and public-data masking for WeCredit. WeCredit is not a TU credit-rating service or a final determination of facts.
WeProperty HK · WeCredit HK (case studies in Traditional Chinese)
Reference sources
Official documents support the technical, risk and measurement concepts. Checklists, estimation methods and illustrative scenarios are WeTech planning recommendations, not official prices or client results. Check the supplier’s current plan and terms. Sources reviewed: (GMT+8)
Related guides
Discuss your project
Share your workflow, systems and goals. We will review scope before discussing deliverables, acceptance and a quotation.
Request an initial consultationExplore this service