Stop defending your team. Start defending the numbers.

Monthly cost impact summaries that translate testing metrics into financial language. Stakeholders see savings projections, cost reduction percentages, and ROI figures — with real-time metrics, defect analysis, and team performance tracking in one unified dashboard. (Internally: the Executive Dashboard.)

Automatically detects causal relationships across your development tools using Quality Impact Chains. Three-layer correlation (referential, temporal, heuristic) with confidence-weighted financial quantification. (Internally: the Cross-Platform Correlation Engine.)

Financial impact calculated across four categories: defect resolution, release acceleration, productivity gains, and infrastructure optimization. Every number is backed by transparent, inspectable methodology. (Internally: the ROI Visualization Engine.)

Burndown analysis, velocity metrics, and sprint-over-sprint comparisons. See how quality practices impact delivery speed and correlate testing investment with sprint throughput.

Compare your defect rates, resolution times, velocity, and effort distribution against industry averages and best-in-class benchmarks grounded in DORA, Boehm, and Capers Jones research.

Filter dashboards by team with auto-discovered teams from your Jira boards. Per-team member counts ensure accurate defects-per-developer and benchmark comparisons.

Capture a metrics baseline at any point and track projected vs actual ROI over time. Measure real improvement in defect rates, velocity, and resolution times.

An immutable, chronological financial record of every quality event. Each entry links back to the defect, release, or pipeline event that produced it — with a confidence score tied to the correlation chain. Baseline resets never delete entries; the trail spans every era of your operation. (Internally: the Quality Cost Ledger.)

Eight recommendation templates generated from your team's metrics — defect volume, resolution time, velocity, bug-fix overhead, PR review latency, pipeline reliability, open-defect inventory, and release cadence. Each card carries a currency-per-month or hours-of-capacity impact estimate with the formula in a tooltip, plus concrete actions tagged by effort. Drag cards through Backlog → In Progress → Done; explicitly Deny ones your team consciously rejects so nothing rots in the backlog. Recommendations that need code-platform data render as locked cards with a "Connect your repositories to unlock" CTA — never silently omitted. (Internally: the Improvements Pipeline.)

Dashboard splits into Overview, Quality, Delivery, Code & CI/CD, Ledger, and Improvements. Each tab focuses on one decision a stakeholder needs to make — execs land on Overview, engineering managers live in Quality + Delivery, finance reviews the Ledger. The Code & CI/CD tab auto-hides when no code platform is connected so Jira-only orgs see a cleaner UI. Active tab persists across reloads.

Switch the team filter and every metric, ROI calculation, and recommendation impact recomputes from that team's raw data — not visually filtered from a pre-aggregated org-wide number. A team's "€34,000/mo bug-fix overhead" reflects that team's headcount, blended rate, and effort split. Switching teams refetches metrics, ROI, and re-seeds improvements in parallel; no stale cards from the previous team linger.

Connect GitHub, GitLab, or Azure Pipelines to replace static estimates with live metrics. ROI calculations use real PR review times, CI/CD pipeline data, and release velocity — not generic industry averages.

GDPR-compliant data handling with EU data residency. Read-only integration credentials, encryption in transit (TLS 1.2+), cryptographic license validation, and least-privilege token scoping.