

Key outcomes
What changed after the engagement.
Faster resolution
Mean time to resolution dropped by 23%.
Trust in AI
Inline explanations increased adoption across teams.
Cleaner ops
Fewer repeat issues via root-cause tagging.
Impact in numbers
- MTTR-23%6-week post-launch average
- Repeat issues-17%YoY vs. similar period
- User adoption+19%Weekly actives
The story at a glance
We shipped an explainable-AI dashboard for anomaly detection. Ops teams see what changed, why it matters, and the fastest path to resolution.
- Faster resolution
Mean time to resolution dropped by 23%.
- Trust in AI
Inline explanations increased adoption across teams.
- Cleaner ops
Fewer repeat issues via root-cause tagging.
Client request and goals
Teams needed actionable AI—not black-box scores. We highlighted context, confidence, and playbooks for each anomaly.
- Product design
- Frontend build
- Telemetry & QA
01
SolutionProblemImprove usability within a legacy backend without major refactors.
What we didRefined information architecture and flows while working within existing constraints, unlocking quick-win UX gains.
Key features
Anomaly timeline
- Change-point markers
- Confidence bands
- One-click compare
Explainability
- Top drivers
- Counterfactual hints
- Linked documentation
Playbooks
- Resolution steps
- Auto-log to ticketing
- Owner routing
Process
- 01
Discovery
- Shadowed on-call rotation
- Interviewed SMEs
- 02
Design & build
- Vertical slices with telemetry
- 03
QA & launch
- Playwright scenarios
- Rollback plan
Highlights
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