Occupation. Product & Engineering
Ship better. Know what sticks. Fix what escapes.
Release velocity, feature adoption, defect escape rate, and customer satisfaction. each tracked in a different tool and none of it easy to bring to a leadership conversation. RapidDashboard gives product and engineering leaders the data to make confident decisions about what to build next.
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Click a prompt. Charts and KPIs update for that scenario (sample data).
Generated from
"Show me release velocity over the last 6 sprints. how many story points shipped vs committed, and what's driving variance?"
- Velocity trending up. three consecutive sprints above the 6-sprint rolling average.
- Sprint 4 gap: 18 pts committed but not shipped. mid-sprint scope addition from a stakeholder request, not accounted in velocity.
AI-generated report (sample)
Release velocity is improving. 91% of committed work shipped in the last sprint, up 4 points. However, 14% of total points across 6 sprints were mid-sprint additions, making velocity an unreliable planning signal. Recommend formalizing a "no mid-sprint scope additions" policy and tracking planned vs unplanned work separately.
Generated from
"Which features shipped in the last 90 days have the highest and lowest adoption rates among active users?"
- AI Summary feature at 9% adoption after 60 days. below the 25% threshold. No in-app discovery prompt was shipped with it.
- Report Export feature at 74% adoption and is the top requested item in support tickets. strong signal for expansion.
AI-generated report (sample)
Report Export is the clear adoption winner at 74%. AI Summary is underperforming at 9% after 60 days. the root cause appears to be discoverability, not value. No in-app prompt was shipped at launch. Recommend a targeted in-app discovery campaign for AI Summary and a feature expansion plan for Report Export based on the support signal.
Generated from
"What is our defect escape rate over the last 6 releases. how many bugs made it to production vs were caught in QA?"
- Release 4.2 had a defect escape rate of 14%. rushed QA cycle due to a hard deadline. P1 incident followed.
- Integration layer has escaped defects in 3 of 6 releases. systemic coverage gap in that module.
AI-generated report (sample)
Defect escape rate is 6.2%, above the 4% target. Release 4.2 drove the worst outcome. a compressed QA cycle led to a 14% escape rate and a P1 incident. The integration layer is a systematic weak spot, appearing in 3 of 6 releases. Recommend adding integration test coverage as a release gate requirement and enforcing QA time minimums before any release.
Generated from
"Show me NPS score broken down by which features respondents mentioned most. what's driving detractor responses?"
- NPS dropped 8 points. detractor responses cluster around "slow load times on the dashboard view," a known performance regression from v4.1.
- Promoter responses heavily cite Report Export and new filtering capabilities. areas to double down on.
AI-generated report (sample)
NPS fell 8 points this quarter, driven by performance complaints concentrated on the dashboard view. a regression introduced in v4.1. Promoters are strongest around reporting and filtering features. Recommend prioritizing the dashboard performance fix in the next sprint and highlighting the Report Export improvements in upcoming customer communications.
How we connect your systems
Product and engineering data is split between your issue tracker, your analytics platform, and your source control. We connect them so feature decisions are data-backed, not anecdotal.
| System | What we pull | Connection path |
|---|---|---|
| Issue Tracking (Linear, Jira) | Sprint velocity, story points, defect counts, release timelines | Official REST APIs. issue data synced into your private data store. |
| Product Analytics (Amplitude, Mixpanel) | Feature adoption, retention cohorts, user flow, event counts | Vendor-supported APIs. behavioral data mapped to feature and release context. |
| Source Control / CI (GitHub, GitLab) | Deployment frequency, lead time, DORA metrics, PR cycle time | Webhook or API. engineering delivery metrics surfaced alongside product outcomes. |
What you can build
Delivery metrics
- Velocity & commit accuracy
- DORA metrics
- Release frequency
Feature outcomes
- Adoption by feature
- Retention impact
- Usage drop-off alerts
Quality
- Defect escape rate
- Production incident trends
- QA coverage gaps
Product reports
- Sprint retrospective
- Quarterly product review
- Board product summary
Product data stays in your environment
Roadmap data, customer behavior telemetry, and quality metrics are core intellectual property. RapidDashboard keeps everything in a private data store . shared with a vendor's AI or multi-tenant analytics platform. Optional enterprise AI configurations designed not to train on your product data or user behavior.