Abloomify Engineering Dashboard Features
Executive Engineering Overview
Get a single view of velocity, risk, and delivery confidence across your engineering organization with the Engineering Velocity Score (EVS).
PR Cycle Time Breakdown
Spot where pull requests stall, shorten review loops, and remove delivery bottlenecks faster with detailed cycle time analytics.
Code Review Health
Monitor review quality and consistency so standards stay high while teams move quickly. Track approval rates and review coverage.
Contributor Impact Trends
Understand contribution patterns across teams and balance workload before burnout appears with the top contributors dashboard.
Velocity Trend Analysis
Track delivery momentum week over week and validate whether process changes improve output with velocity trend charts.
Task Throughput Visibility
Compare planned versus completed work and keep roadmap commitments grounded in real capacity with task velocity metrics.
DORA Metrics with Performance Bands
Abloomify tracks all four DORA metrics directly from GitHub: deployment frequency, lead time for changes, change failure rate, and mean time to recovery (MTTR). Every metric is scored against Elite, High, Medium, and Low performance bands so engineering leaders know exactly where they stand against industry benchmarks. Change failure rate is computed automatically, failed production deployments and reverts are detected and shown alongside successful deploys in a deployments drill-in, and lead time is measured from first commit to production. Teams that do not formally tag deployments still get delivery numbers through deploy-workflow and merge-to-release-branch detection, and the dashboard discloses when a number is estimated rather than read directly. You can define what counts as a production deployment per repository and branch.
CI/CD Pipeline Health
The CI/CD Health view reads GitHub Actions to show build success rate, run and failure counts, average duration, and queue time, with a per-workflow breakdown and automatic flaky-test candidate detection. Engineering leaders can see which pipelines are slow, unreliable, or wasting compute, and trend pipeline reliability over time.
Security Posture
The Security Posture view consolidates open Dependabot and code scanning alerts by severity, with a severity mix, an open-alerts- over-time trend, and average time to remediate. A prioritized "fix these first" list ranks the most severe open vulnerabilities by CVSS score and EPSS exploit probability, and shows the GHSA/CVE identifier, the affected repository, and whether a patched version is available, so security and engineering leaders triage real risk instead of raw alert counts.
AI-Assisted Engineering ROI (Copilot, Cursor, Claude Code)
Abloomify is one of the few platforms that measures the return on AI coding assistants. The AI-Assisted Engineering view surfaces adoption and impact signals from GitHub Copilot, Cursor, and Claude Code where those tools are connected: who is actually using AI, AI-assisted lines versus human-written lines, acceptance rates, and estimated cost. An AI-versus-human cohort comparison shows whether heavy AI adopters ship more and review faster, and an AI Leverage measure feeds the Engineering Velocity Score when AI coding data is present (and leaves the score unchanged when it is not). Engineering leaders finally get a defensible answer to "is our AI tooling spend paying off?"
PR Flow Analytics
The PR Flow view reports median time to first review, PR cycle time, self-merge rate, open-PR aging, and PR size distribution, plus a Size view with total pull requests, total lines changed, and your largest pull requests. It pinpoints exactly where pull requests stall, who is merging without review, and which oversized PRs are slowing delivery.
AI Engineering Analyst - Bloomy Query Examples
Scenario 1: Sprint Retrospective Summary
User prompt: "Give me a sprint retrospective summary: velocity trends, blockers, and wins from the last 4 weeks"
- Weekly Velocity: PRs Merged increased from 2 to 13 over 4 weeks
- Total PRs Merged: 26 (up 117%), Commits: 250 (up 56%), Avg Cycle Time: 9.2 hrs (Healthy)
- Wins: EventBridge migration shipped, security hardening complete, AI integration framework landed
- Blockers: Large refactor PRs slowing reviews, dependency vulnerabilities needed unplanned fixes
Scenario 2: Team Workload Analysis
User prompt: "Who on my team is overloaded right now?"
- Workload Distribution: Sarah L. (32%), Mike T. (28%), Alex K. (22%), Others (18%)
- Risk Assessment: Sarah has 8 open PRs and 12 reviews pending (High risk)
- Alert: Sarah has 2x the review load of team average. Consider redistributing 4 reviews.
- Tip: Alex has capacity for 2-3 more PR assignments this sprint.
Scenario 3: PR Bottleneck Detection
User prompt: "Where are our PRs getting stuck?"
- Cycle Time Breakdown: Coding (4.2 hrs), Waiting for Review (18.6 hrs), In Review (3.1 hrs), Merge Queue (1.4 hrs)
- Stalled PRs: Auth refactor #412 waiting 3 days (no reviewer), API v2 migration #398 waiting 2 days (needs security)
- Insight: Review wait time is 68% of total cycle time. PRs over 500 lines take 3x longer to review.
- Action: Break large PRs into smaller chunks and assign dedicated reviewers for critical paths.
Scenario 4: Code Review Health Analysis
User prompt: "How's our code review health this sprint?"
- Review Outcomes: Approved (78%), Changes Requested (18%), No Review (4%)
- Metrics: Approval Rate 88% (+5%), Avg Review Time 4.2 hrs (-1.3 hrs), Coverage 94% (+2%)
- Success: Review times improved 24% after implementing the review rotation last sprint.
- Watch: 4% of PRs merged without review (all from hotfix branch). Consider requiring 1 approval for hotfixes.
Scenario 5: Quarterly Velocity Comparison
User prompt: "Are we shipping faster than last quarter?"
- Monthly PRs Merged: Jan (42), Feb (58), Mar (71) showing upward trend
- Quarter over Quarter: PRs Merged +34% (128 to 171), Cycle Time -25% (32 to 24 hrs), Deploy Frequency +62% (2.1 to 3.4/week)
- Success: Shipping velocity up 34%. Biggest driver: reduced PR cycle time from 32 to 24 hours.
- Insight: Deploy frequency doubled after moving to trunk-based development. Teams shipping 3.4x per week on average.
Engineering Intelligence Platform Features
- Engineering Velocity Score (EVS): a tunable composite score across five pillars - Output, Speed, Quality, Collaboration, and AI Leverage - with weights and thresholds engineering leaders can configure
- DORA Metrics: deployment frequency, lead time for changes, change failure rate, and mean time to recovery, each scored against Elite, High, Medium, and Low performance bands
- CI/CD Health: GitHub Actions build success rate, duration, queue time, and per-workflow flaky-test detection
- Security Posture: open Dependabot and code scanning alerts by severity, CVSS and EPSS exploit probability, and a prioritized fix- first list with patch availability
- AI-Assisted Engineering: adoption, AI-assisted versus human lines, acceptance rate, and cost across GitHub Copilot, Cursor, and Claude Code, with an AI-versus-human cohort comparison
- PR Cycle Time Analytics: Track time from PR open to merge, identify review bottlenecks and stalled PRs
- Code Review Health: Monitor approval rates, review coverage, and comment quality
- Top Contributors Dashboard: Recognize high performers and identify overloaded team members
- Repository Activity: PRs merged, commits, and net lines changed by repo, team, and contributor
- Task Velocity: Issues opened vs closed, completion rates, and sprint burndown
Engineering ROI Metrics
- 30% Faster PR Cycle Time by identifying review bottlenecks
- 2x Code Review Coverage with balanced reviewer assignments
- 15 hours Manager Time Saved per month on reporting
- 40% Better Velocity Visibility across all engineering teams
