Engineering Delivery Intelligence
Turn Jira delivery data into clear insights about flow, carryover, bottlenecks, scope changes, requirements, and production delivery.
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Sprint 24 — delivery health
31 issues entered this sprint as carryover. 19 of them are in code review or QA right now.
Code review · 14 issues — every number opens the Jira issues behind it
More than whether work was completed — the story behind delivery:
Where work slows down, what carries over, where scope changes, which stages accumulate work, and what actually reaches production.
Jira records what happened. VeloWise turns it into where work went, why it carried over, and what is slowing delivery down.
What the export contains
What it finds Example delivery data
What it answers
38% of this sprint was inherited — 19 of those 31 issues are already past development, waiting on review or testing.
Understand sprint carryover →Current sprint scope · 82 issues
Where is the carryover now?
Select any part of the chart — in the product it opens the Jira issues behind it.
Engineering may not be the constraint: 63% of started, unfinished work is already in code review or QA — both well above their usual queue.
Find delivery bottlenecks →Started, unfinished work — now vs usual for this point in the sprint
Usual queue (median of the last 6 sprints). A tall bar alone is not a bottleneck; a queue well above its own history is.
5 issues have carried across 3 or more sprints. That is delivery debt, not spillover.
Separate spillover from WIP debt →Select any part of the chart — in the product it opens the Jira issues behind it.
“Done” does not always mean delivered: Application Workflow is 92% past development but only 42% released.
Track epic delivery →Application Workflow
Reporting
User Management
Select any part of the chart — in the product it opens the Jira issues behind it.
Carryover rose for 4 consecutive sprints while commitment stayed between 60 and 64 issues.
Choose useful delivery metrics →Select any part of the chart — in the product it opens the Jira issues behind it.
The team didn’t simply miss 36 issues — scope grew 28% after the sprint started.
Explain sprint scope change →31 of the 36 unfinished issues carried into Sprint 24; the rest went back to the backlog.
From export to explanation
Create an organization and project, then drop in a Jira export. Fields are detected automatically; anything unusual can be mapped by hand and is remembered for next time.
Sprint health, scope change, carryover, bottlenecks and cycle time — each conclusion labelled by how well your data supports it.
A deterministic summary with recommendations tied to the findings, a commitment simulator, and exports for your sprint review.
Files are parsed and analyzed in your browser. Signed out, datasets stay in browser storage. With an active organization project, imports are also saved for your team. Owners and admins can delete imports; browser data and organization data have separate deletion controls.
Honest by design
A standard Jira CSV has no sprint dates and no status history. Instead of drawing a convincing but invented chart, every conclusion carries one of three labels — and missing data comes with instructions for getting it.
Read directly from your export or the sprint dates you provided. “23 issues entered from previous sprint carryover.”
Derived with a stated assumption. “At least 8 issues were added after start” — from creation dates, so a lower bound.
Your export can’t support it. “Time in code review needs status-transition history” — and how to export it.
Guides
Practical guides for engineering managers, directors, Scrum Masters, TPMs and product managers.
Understand missed commitments with Jira delivery signals: carryover, scope growth, capacity, review and QA queues, release waits and requirement-driven rework.
Read guide →What a useful sprint report contains beyond the Jira burndown: commitment, scope change, carryover reasons, the constraint, and one or two decisions — with evidence levels stated.
Read guide →How to calculate sprint carryover from a Jira export, classify it by reason (review, QA, release, blocked), and tell the difference between observed and inferred causes.
Read guide →How to find delivery bottlenecks in an engineering workflow using queue size and time-in-stage, why arbitrary red/amber/green thresholds mislead, and what to do once you find the constraint.
Read guide →Plan sprint commitments against historical throughput ranges instead of optimistic capacity math. Include existing WIP and typical scope growth, and communicate risk honestly.
Read guide →Create a free account, set up your organization and project, and import your Jira CSV for your first analysis. Or explore the sample first: it simulates ten closed sprints of a fictional checkout team — healthy sprints, scope creep, a review bottleneck, a QA crunch, a release freeze, and commitment creeping above steady throughput.