Challenge
Internal QA audit data lived in raw database records with no structured reporting layer. Leadership needed actionable visibility into team performance without digging through spreadsheets.
Executive productivity reporting
QA AI: Overall Productivity
An executive-facing productivity dashboard that translates internal QA audit data into actionable metrics, trend analysis, and team-level performance tracking for C-level stakeholders.
Next.jsTypeScriptPrismaMariaDBData viz
Stack
Next.js, TypeScript, Prisma, MariaDB
Audience
C-level and operations teams
Result
Business-readable QA insights
Client context
S.P. Madrid & Associates
Category
Full stack dashboard with data visualization
Executive productivity reporting2025–2026
QA AI: Overall Productivity
Raw audits into executive clarity.
Business-readable QA insights
Case study
The challenge, the build, and why it holds up.
Response
Built an executive-facing dashboard with overview metrics, trend analysis, filtered breakdowns, and ranking views — turning operational data into reports that stakeholders could act on immediately.
Why it holds up
The Prisma-backed data layer and modular report components mean new metrics and breakdowns can be added without rearchitecting the system. The dashboard scales with the team.
Deliverables
- Executive overview metrics and KPI cards
- Trend analysis with weekly and monthly tracking
- Filtered breakdowns and ranking views
- Role-based data access for C-level and team leads
Why it matters
Improved visibility into QA system performance by translating raw database records into business-readable insights used across leadership and operations.
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