Opportunity Queue
The cross-cloud triage surface — every detection lands here as a costed, scored finding with SLA tracking, compliance mapping, and evidence export.
At a glance
| Route | /findings |
| Group | Operate |
| Page permission | findings |
What it is
The cross-cloud triage surface. Every detection from every provider lands here as a costed, scored finding, and this is where a team decides what to act on, what to defer, and what to accept — with SLA tracking, compliance mapping, and evidence export throughout.
Who it is for
FinOps practitioners and platform engineers running the day-to-day optimisation cycle, plus compliance teams who need the evidence trail.
How it works
The waste engine executes the detector registry — 305 active rules across AWS, Azure, GCP, OCI, Kubernetes, and cross-cloud patterns — against live inventory, ingested telemetry, and the normalised ledger. Scans run on a schedule constrained by your plan's minimum interval, and can also be triggered manually.
What every finding carries
Estimated monthly saving
What fixing it is worth.
Confidence score
Between zero and one. How certain the detection is.
Effort score
How much work the fix represents.
Risk assessment
What could go wrong.
Recommended action
What specifically to do.
Composite priority
Savings weighted by confidence, divided by effort.
The remediation class taxonomy
Why this taxonomy saves so much time
Nobody has to investigate whether a fix can be automated — the queue already says so. That is often the slowest step in a manual triage process.
SLA tracking
SLA tracking measures how long findings sit unresolved and surfaces the most overdue by breach duration. A triage narrative digest summarises the queue in prose, and root-cause and what-if analysis are available per finding.
Features
- Unified cross-cloud finding queue with composite priority ranking
- Per-finding savings estimate, confidence score, effort score, and risk assessment
- Remediation class taxonomy — advisory, guided, automatable
- Measured utilisation evidence rendered as a sparkline with p95, average, minimum, and maximum
- SLA tracking with an aging monitor and most-overdue ranking
- Root-cause analysis and what-if impact analysis per finding
- AI-generated triage narrative summarising the queue
- Bulk status updates across many findings at once
- Direct auto-remediation trigger from the finding
- Compliance summary mapping findings to control frameworks
- One-click evidence export for audit
How to use it
Run a scan, or confirm the scheduled one completed
For every connected provider.
Sort by composite priority and work top-down
The ranking already accounts for savings, confidence, and effort. Do not re-do that arithmetic by hand.
Filter to automatable for the fastest wins
Route those to Optimization for execution.
For guided findings, use the supplied command
Lambda memory rightsizing, for example, carries the exact
aws lambda update-function-configuration invocation with the recommended value.
For advisory findings, assign an owner
Through Ownership, and track to closure.
Always check the sparkline before rightsizing
Validate the measured utilisation series against your team's dashboards where the workload is unfamiliar to you.
Bulk-dismiss deliberate classes of finding
So the queue stays credible. A queue full of known-intentional items stops being read.
Monitor the aging panel weekly
A growing SLA breach count means triage capacity is short, not that detection is wrong. These are different problems with different fixes.
Export evidence when an audit requires it
One click, from this page.
Why it matters
Most organisations know they have waste but cannot say where it is, what it is worth, or which item to fix first. This module answers all three continuously and across every cloud at once.
The remediation class taxonomy means nobody wastes time investigating whether a fix can be automated — the queue already says so.
Connects to
- Feeds Optimization and Autopilot
- Evidence supports Compliance
- Owners come from Ownership
- Scan cadence is set per plan limits in Membership
- Incidents escalate to Apex → Incidents
- Receives findings from FinSecOps, Database Health, Kubernetes, API Monitoring, and AI Infrastructure
CEO View
A deliberately minimal executive snapshot — the biggest waste source, the ROI narrative, and the one action that should happen next.
Autopilot
An autonomous cost optimisation agent that acts within explicitly defined guardrails, with three graduated trust modes so you adopt autonomy at your own pace.