Operational dossier

Team Pulse dashboard for QA performance

Built a QA analytics platform that unified Jira and TestRail data, reducing blind spots and giving leaders clearer release and team-performance signals.

Report metadata

Analytics QA AI Tooling SaaS

2024-07

Solutions Architect

Automated quality review made bug-report standards more consistent

Unified Jira + TestRail visibility reduced release blind spots

Team performance signals became easier to compare and coach

Next.js Firebase Genkit Gemini Recharts

Challenge

QA and engineering leaders lacked a single source of truth for team performance. Jira showed bug counts and TestRail showed execution logs, but there was no reliable way to connect quality, efficiency, and impact across teams.

Approach

Built a unified dashboard that ingests Jira and TestRail data via API or CSV.
Added an LLM-based quality review (Gemini via Genkit) to score bug clarity and reproducibility.
Designed a multi-factor scoring model that rewards valid bugs and penalizes noise.
Visualized time-based and execution efficiency per project and contributor.

Results

Managers gained a consistent, data-driven way to coach performance and reduce noise.
Cross-project visibility improved, highlighting bottlenecks and training gaps quickly.
Quality scoring made release and performance reviews faster and more objective.

Key metrics

Measured impact

Metric	Before	After
Bug review effort	Manual, inconsistent	Automated LLM scoring
Visibility	Siloed Jira/TestRail	Unified dashboard
Performance insight	Subjective	Multi-factor scoring

Implementation notes

Quality scoring works only with shared definitions and review criteria.
Time-based efficiency reveals training needs faster than raw throughput.
Dashboards must translate data into coaching actions, not just metrics.