AI Recommendations

Beyond detecting problems, ArcIn proactively recommends improvements — right-sizing, query fixes, risky-deploy warnings, and reliability gaps — each with the evidence and expected impact so you can prioritise with confidence.

Types of recommendations#

• Efficiency: over-provisioned hosts/pods and idle cloud resources to right-size.
• Performance: N+1 queries, slow endpoints, and cache opportunities.
• Reliability: single points of failure, missing alerts, and deploys with elevated risk.

Evidence and impact#

Each recommendation includes the data behind it and an estimated impact (e.g. "~35% CPU headroom reclaimable" or "removes 1,200 redundant queries/min"). You can accept, snooze, or dismiss — and dismissals feed IntelliTune.

Where they appear#

Recommendations surface on the relevant entity page and in a central Recommendations inbox. High-impact reliability items can be routed to Slack or a ticket automatically via workflows.