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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
Overview

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.

Highlights
  • 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.

Business needs

Client request and goals

Teams needed actionable AI—not black-box scores. We highlighted context, confidence, and playbooks for each anomaly.

Key objectives
  • Product design
  • Frontend build
  • Telemetry & QA
Success criteria
-23% · MTTR-17% · Repeat issues+19% · User adoption
Challenges and solutions
  • 01
    Problem

    Improve usability within a legacy backend without major refactors.

    Solution
    What we did

    Refined 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

Let’s talk terraanalytics — ml insights for field operations

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