Everything you need to understand
engineering delivery

From individual contributor metrics to portfolio-level trends, Systemi gives every engineering leader the visibility they need to make informed decisions.

systemi.app/overview

Throughput

24.3items/wk
+12%

Cycle Time

3.2days P50
-18%

WIP

8items
-2

Cycle Time Distribution

0dP50: 3.2dP80: 6.1d14d+

Cycle Time & Throughput

Measure how long work takes from start to done and how much gets delivered. Break it down by phase — queue, in-progress, review, merge — to find exactly where time is lost.

  • P50, P80, and P90 cycle time breakdowns for Jira and PRs
  • Throughput trends with week-over-week comparisons
  • Distribution histograms to spot variability
  • Anomaly detection flags outlier work items automatically
Work Journey
PROJ-142Done
6.5d total
1.5d
2.5d
1.5d
1d
PROJ-187In Review
4.2d total
0.5d
2.8d
0.9d
PROJ-203QA
8.1d total
3d
3.2d
1.2d
0.7d
Backlog
In Progress
In Review
QA
Done

Contributor & Team Analytics

Drill into any team or individual. Compare performance side-by-side, spot coaching opportunities, and celebrate wins — all backed by real data from your tools.

  • Individual contributor dashboards with key metrics
  • Health status indicators (green / amber / red) per metric
  • IC Capacity percentage for managers who also code
  • Search and filter contributors by name, team, or department
Contributors
SC
Sarah Chen

Senior Engineer

5.2/wk

Thrpt

2.1d

Cycle

4.8/wk

PRs

MJ
Marcus Johnson

Staff Engineer

3.8/wk

Thrpt

4.5d

Cycle

3.2/wk

PRs

PP
Priya Patel

Engineering Manager

1.2/wk

Thrpt

1.8d

Cycle

0.5/wk

PRs

AI-Powered Insights (Optional)

Every metric comes with an AI Explain button that analyzes your data and tells you what's happening and why. When a metric is amber or red, get actionable improvement suggestions. AI features are fully optional — the platform works without them, and they can be disabled at any time.

  • One-click metric explanations powered by AI
  • Improvement suggestions when metrics need attention
  • Trend analysis with context-aware recommendations
  • All insights are PII-free — no personal data sent to AI
  • Completely optional — requires your own OpenAI API key
AI Insight
Cycle Time AnalysisAmber

Your Jira cycle time P80 increased from 5.2 days to 7.8 days over the last 30 days. Key observations:

  • 3 items spent >5 days in "In Review" (review bottleneck)
  • Average WIP increased to 12, above the healthy threshold of 8
  • Queue time increased 40% — items waiting longer before being picked up
Suggested Improvement

Set a review SLA of 24 hours and reduce WIP to 2 items per contributor. This typically cuts P80 cycle time by 30–40%.

Forecasting & Capacity Planning

Project future delivery based on historical velocity. Understand what your team can realistically deliver in the next sprint or quarter with confidence intervals.

  • Velocity-based delivery projections with confidence bands
  • Sprint and quarter-level capacity forecasts
  • Conservative vs typical estimates for stakeholder planning
  • Historical accuracy tracking improves over time
Capacity Forecast

Typical

27

items/wk

Conservative

21

items/wk

Confidence

85%

based on 90d

352515
18
22
19
26
24
27
29
26
W1W2W3W4W5W6W7W8
Actual
Forecast
Confidence band

Connects to your existing tools

Read-only access to Jira, GitHub, and Slack. Your workflow is never modified. Set up in minutes.

📋
Jira
🐙
GitHub
💬
Slack
Read-only sync
Systemi Analytics Engine
MetricsAIForecastsAlerts
Dashboard
Reports
Email

And much more

Built for engineering leaders who care about process improvement, not micromanagement.

Bottleneck Detection

Automatically surfaces where delivery is slowing down — review queues, overloaded contributors, or stale items.

Automated Reports

Schedule PDF reports to stakeholders' inboxes — daily, weekly, or monthly with full metric breakdowns.

Work Explorer

Browse and filter every Jira ticket, PR, and commit. Click any name to jump to their contributor profile.

Privacy First

No message content, no code content, no ticket descriptions stored. Only metadata needed for metrics.

Self-Hosted Option

Deploy on your own infrastructure with Docker. All data stays on your servers. No source code in the image.

Multi-Tenant

Complete data isolation between organizations. Each team sees only their own data with role-based access.

Ready to see your engineering metrics?

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