Product & engineering dashboards
Ship faster and see what sticks
Delivery data lives in Jira and GitHub, usage in Amplitude and feedback in a survey tool. RapidDashboard joins them in a private data store and answers plain-English questions with a full product dashboard.
Live examples
One question, one full dashboard
Pick a question. RapidDashboard answers with KPIs, charts, the records behind them and an AI summary of what to do next. Hover any chart for values or open its table view.
How fast are we shipping? Show deployment frequency, cycle time by team and sprint commitments for the last 12 weeks.
Engineering delivery
Deploys per week
38
+14 vs 12 weeks ago
Cycle time
3.4 days
-1.2 days first commit to production
Change failure rate
4.8%
-1.1 pts vs prior 12 weeks
Commit vs ship
91%
+4 pts Sprint 40
- Deploys
- Failed or rolled back
| Deploys | Failed or rolled back | |
|---|---|---|
| W29 | 24 | 2 |
| W30 | 26 | 2 |
| W31 | 27 | 2 |
| W32 | 29 | 2 |
| W33 | 28 | 1 |
| W34 | 31 | 2 |
| W35 | 30 | 2 |
| W36 | 33 | 2 |
| W37 | 34 | 2 |
| W38 | 35 | 2 |
| W39 | 36 | 2 |
| W40 | 38 | 2 |
- Coding
- Pickup wait
- Review
- Deploy
| Coding | Pickup wait | Review | Deploy | |
|---|---|---|---|---|
| Platform | 1.2d | 0.6d | 0.5d | 0.3d |
| Growth | 1.4d | 0.9d | 0.6d | 0.2d |
| Mobile | 1.5d | 0.8d | 0.6d | 0.6d |
| Integrations | 1.8d | 1.6d | 0.8d | 0.4d |
| Sprint | Committed | Shipped | Added mid-sprint | Commit vs ship | Status |
|---|---|---|---|---|---|
| Sprint 36 | 50 pts | 45 pts | 5 pts | On plan | |
| Sprint 37 | 52 pts | 34 pts | 14 pts | 18 pts carried over | |
| Sprint 38 | 48 pts | 44 pts | 6 pts | On plan | |
| Sprint 39 | 50 pts | 46 pts | 5 pts | On plan | |
| Sprint 40 | 54 pts | 49 pts | 4 pts | Highest output |
What changed and what to do
- Warning: Integrations has the longest cycle time at 4.6 days, and 1.6 of those days are PRs waiting for a first review.
- Info: Sprint 37 shipped 34 of 52 committed points after 14 points of stakeholder requests arrived mid-sprint.
- Good: Deploys rose to 38 a week while change failure rate fell to 4.8%, so speed and stability improved together.
AI summary
The team now ships 38 production deploys a week with a 3.4-day cycle time and a 4.8% change failure rate, all better than last quarter. Sprint 40 delivered 91% of committed points. Integrations is the slowest team because PRs wait 1.6 days for review, and mid-sprint additions made up about 14% of recent work. Recommend a review rotation for Integrations and tracking planned and unplanned points separately.
Ask next
- Lead time for changes by repo
- Who reviews Integrations PRs?
- Show unplanned work by requester
Which features shipped this year have the highest and lowest adoption, and how many new signups reach activation?
Activation & feature adoption
Activation rate
46%
+6 pts first dashboard in 14 days
DAU/MAU
41%
+3 pts vs last quarter
Week-8 retention
59%
+16 pts July vs February cohort
Features shipped
8
2 below 25% adoption at 60 days
Adoption threshold at 60 days: 25%
- Signed up 1,240
- Connected a data source 930 75%
- Built first dashboard 570 61%
- Invited a teammate 410 72%
- Active in week 4 330 80%
- Converted to paid 112 34%
What changed and what to do
- Critical: AI Summary sits at 9% adoption after 60 days, below the 25% threshold. It shipped without an in-app discovery prompt.
- Good: Report Export reached 74% of active accounts and is the most requested topic in support, a strong signal for a v2.
- Info: Week-8 retention climbed from 43% to 59% across cohorts since the onboarding checklist launched in May.
AI summary
Activation reached 46% of September signups and DAU/MAU rose to 41%. Report Export leads adoption at 74%, while AI Summary trails at 9% after 60 days, which points to discoverability. The biggest funnel drop is between connecting data and building a first dashboard, where 360 accounts stall. Recommend an in-app discovery prompt for AI Summary and a dashboard template step right after data connection.
Ask next
- Adoption of AI Summary by plan
- Where do signups stall after connecting data?
- Retention for accounts using Report Export
What is our bug escape rate over the last six releases? How many bugs reached production vs were caught in QA?
Release quality
Bug escape rate
6.3%
+2.3 pts vs 4% target
P1s in production
2
both in 4.2 last 90 days
Test coverage
78%
+3 pts target 90%
Escaped bugs open
4
-3 of 20 escaped
- Caught in QA
- Escaped to production
| Caught in QA | Escaped to production | |
|---|---|---|
| 4.0 | 46 | 2 |
| 4.1 | 52 | 4 |
| 4.2 | 43 | 7 |
| 4.3 | 49 | 2 |
| 4.4 | 55 | 4 |
| 4.5 | 51 | 1 |
78%
lines covered · target 90%
| Bug | Summary | Module | Release | Severity | Status |
|---|---|---|---|---|---|
| NB-2402 | Dashboard view slow above 40 widgets | Dashboard UI | 4.1 | P2 | Open 41 days |
| NB-2388 | OAuth token refresh fails after 24 hours | Integrations | 4.4 | P2 | In progress |
| NB-2423 | HubSpot field mapping resets on save | Integrations | 4.5 | P3 | In review |
| NB-2417 | CSV export drops time zone | Reports | 4.4 | P3 | Scheduled 4.6 |
| NB-2291 | Webhook retries drop events | Integrations | 4.2 | P1 | Fixed in 4.2.1 |
| NB-2304 | Salesforce sync duplicates rows | Integrations | 4.2 | P1 | Fixed in 4.2.2 |
What changed and what to do
- Critical: Release 4.2 let 7 of 50 bugs through, a 14% escape rate, after a compressed QA cycle. Both P1s of the last 90 days came from it.
- Warning: The integrations module had escaped bugs in three of six releases, which points to a coverage gap in that layer.
- Good: Release 4.5 escaped a single bug in 52, a 1.9% rate, the best result of the period.
AI summary
Across releases 4.0 to 4.5, 20 of 316 bugs reached production, a 6.3% escape rate against a 4% target. Release 4.2 drove the worst result at 14% with two P1s, and the integrations module repeats as the weakest area. Coverage is improving at 78%. Recommend integration test coverage as a release gate and a minimum QA window before every release.
Ask next
- Coverage by module
- Time to fix escaped bugs
- Which customers hit NB-2402?
Show the Q4 roadmap with status, how we delivered against last quarter's plan, and where engineering time is going.
Roadmap & capacity
Q4 items on track
5 / 7
2 at risk
Q3 plan delivered
82%
9 of 11 roadmap items
Time on roadmap
64%
+6 pts of story points
Time on bugs
14%
-3 pts of story points
- Roadmap172 pts64%
- Bugs38 pts14%
- Tech debt32 pts12%
- Support and upkeep27 pts10%
| Segment | Value | Share |
|---|---|---|
| Roadmap | 172 pts | 64% |
| Bugs | 38 pts | 14% |
| Tech debt | 32 pts | 12% |
| Support and upkeep | 27 pts | 10% |
What changed and what to do
- Critical: Salesforce two-way sync is two weeks late. It waits on the new integration test gate, which also protects against the 4.2-style escapes.
- Warning: Mobile offline mode slipped its design review by a week and now carries less than a sprint of buffer.
- Good: Delivery against plan improved from 70% in Q4 2025 to 82% in Q3, and roadmap time rose to 64% as bug work fell.
AI summary
Five of seven Q4 roadmap items are on track, and the dashboard performance fix ships October 12. Salesforce two-way sync is two weeks late and mobile offline mode is at risk. Q3 delivered 9 of 11 planned items, and 64% of engineering time now goes to roadmap work. Recommend moving one Platform engineer to Integrations until the test gate lands and confirming the offline mode scope this week.
Ask next
- What slips if Salesforce sync moves to Q1?
- Roadmap items by customer request
- Show tech debt by service
Break down NPS by the features respondents mention. What moved the score this quarter and what drives detractors?
NPS & feature sentiment
NPS
+34
-8 vs Q2
Promoters
54%
-3 pts scores 9 to 10
Detractors
20%
+5 pts scores 0 to 6
Responses
612
+48 22% response rate
| Step | Change |
|---|---|
| Q2 NPS | 42 |
| Load times | -9 |
| Pricing | -2 |
| Support | -1 |
| Report Export | +3 |
| Filtering | +1 |
| Q3 NPS | 34 |
| Item | Weekly users of feature | NPS among users |
|---|---|---|
| Dashboard view | 92% | 18 |
| Report Export | 74% | 61 |
| Saved filters | 58% | 52 |
| Scheduled reports | 41% | 47 |
| Slack alerts | 33% | 44 |
| Custom metrics | 27% | 38 |
| Embedded share | 22% | 41 |
| AI Summary | 9% | 29 |
What changed and what to do
- Critical: Slow dashboard loading took 9 points off NPS. Comments trace it to the 4.1 regression tracked as NB-2402.
- Warning: The dashboard view is the most used feature at 92% of weekly users and has the lowest NPS among its users at +18.
- Good: Report Export and filtering drive promoter comments and added 4 points to the score.
AI summary
NPS fell 8 points to +34 this quarter, with promoters at 54% and detractors at 20%. Slow dashboard loading accounts for 9 points of the drop and links directly to the 4.1 performance regression. Report Export and filtering lift the score. Recommend shipping the performance fix on October 12 as planned and featuring Report Export in the next customer update.
Ask next
- NPS by plan and company size
- Detractor comments about pricing
- Did fixed accounts change their score?
Sample data for a fictional company. Your dashboards run on your own connected systems.
Sound familiar?
Where the numbers live today
Which launches moved the needle
Answering it means joining Amplitude events, CRM notes and support exports for each feature.
Quality is tracked in pieces
Bugs caught in QA live in Jira, escaped bugs in incident notes, and the combined trend appears at quarterly review.
Velocity moves with scope
Mid-sprint additions change what the velocity number means from one sprint to the next.
Feedback arrives quarterly
Linking NPS comments to specific features and releases takes a week of manual tagging.
Metrics glossary
The metrics behind these dashboards
How each number is defined, so everyone reads it the same way.
- Activation rate
-
The share of new signups that reach the first moment of value you define, within a set window.
FormulaSignups reaching activation event ÷ Total signups
- DAU/MAU (stickiness)
-
How many monthly active users return on a typical day, a common measure of habitual use.
FormulaDaily active users ÷ Monthly active users
- Feature adoption rate
-
The share of active users or accounts that use a feature in a period.
FormulaUsers of feature ÷ Active users
- Cycle time
-
Elapsed time for a change to go from first commit (or work started) to running in production.
FormulaProduction deploy time − Work start time
- Deployment frequency
-
How often code reaches production. It is one of the four DORA metrics, which are widely cited for software delivery performance.
FormulaProduction deploys ÷ Time period
- Bug escape rate
-
The share of bugs found after release instead of before it.
FormulaBugs found in production ÷ (Bugs found before release + in production)
Connections
How your systems connect
Supported APIs and exports sync into a private RapidDashboard store you approve. Dashboards read from that store, so source systems keep their normal load.
Jira / Linear
PullsIssues, sprints, story points, bugs, roadmap items
Official REST and GraphQL APIs on a scheduled sync into your private store.
GitHub / GitLab
PullsPull requests, reviews, deploys, DORA metrics
Webhooks and APIs, so delivery speed lines up with the work items.
Amplitude / Mixpanel
PullsActivation events, feature usage, retention cohorts
Export APIs mapped to features and releases.
LaunchDarkly
PullsFeature flags, rollout percentages, release dates
API sync that ties adoption to the moment a feature reached users.
What you can build
Dashboards and reports teams build next
Delivery
- DORA metrics by team
- Cycle time by stage
- Sprint commit vs ship
Product outcomes
- Activation funnel
- Feature adoption at 30 and 60 days
- Cohort retention heatmap
Quality
- Bug escape rate by release
- Escaped bugs by module
- Test coverage trend
Automated reports
- Sprint retrospective pack
- Quarterly product review
- Board product summary
Roadmaps and telemetry stay yours
Roadmap plans, source control activity and customer behavior data are core intellectual property. RapidDashboard keeps them in a private data store with role-based access, separate from any shared analytics platform. AI features are optional and run on enterprise endpoints whose terms prohibit training on your product data.
FAQ
Product & Engineering dashboards: common questions
What should a product management dashboard show?
Most product leaders track activation, feature adoption, retention cohorts, delivery speed and quality, plus customer sentiment such as NPS by feature. RapidDashboard builds each view from a plain-English question against your live product, engineering and survey data.
How is DAU/MAU calculated?
Divide daily active users by monthly active users, usually as averages over the same month. The result shows how many monthly users come back on a typical day, which makes it a quick read on habit.
What are the DORA metrics?
The four DORA metrics are deployment frequency, lead time for changes, change failure rate and time to restore service. They are widely cited as measures of software delivery performance, and RapidDashboard calculates them from GitHub or GitLab and your incident data.
Can RapidDashboard connect to Jira, Amplitude and GitHub?
Yes. Jira, Linear, GitHub, GitLab, Amplitude, Mixpanel and LaunchDarkly connect through their official APIs. Data syncs on a schedule into a private store, so delivery, usage and quality line up by feature and release.
How is bug escape rate calculated?
Bug escape rate is the number of bugs found in production divided by all bugs found for that release, before and after it shipped. Tracking it per release and per module shows where testing needs reinforcement.
Keep exploring
Related example dashboards
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