VeloWise features: engineering delivery intelligence from Jira
VeloWise turns a Jira export into answers about delivery: what the sprint committed to and delivered, why work carried over, where it is waiting, whether initiatives reach production and which requirements will cause questions. Every report links to the issues behind it and to the method that produced it.
What VeloWise analyzes
Sprint intelligence
Did the sprint deliver what it committed to?
Original commitment, work added and removed, completed and released work side by side — plus live sprint progress showing which issues put the plan at risk.
Carryover analysis
Why did work move to the next sprint?
Carryover grouped by the delivery stage each issue was in at sprint close — development, code review, QA or release — and the issues that keep carrying sprint after sprint.
Bottleneck analysis
Where is work accumulating?
Current queues in each workflow stage compared with that stage’s own history, so a growing review or QA queue stands out without arbitrary red/amber/green thresholds.
Epic delivery
Is the initiative actually reaching production?
Epic progress by delivery stage — in development, code review, QA/test, done and awaiting release, and released — so “done” is never mistaken for delivered.
Requirements quality
Which stories will raise questions mid-sprint?
Stories and bugs checked for acceptance criteria, actor, expected behaviour, error handling, edge cases and testability — with the evidence for every flag.
Requirements quality guideHow requirement signals are detected
Capacity and commitment
How much can the team realistically take on?
Historical throughput ranges, inherited work in progress and typical scope growth, with a simulator to test a proposed commitment before the sprint starts.
Scope change
How much did the plan change after the sprint started?
Work added and removed after sprint start, reconstructed from change history where available and labelled as a lower bound where it is not.
Cycle time and aging work
How long does work take, and what is getting old?
Cycle time percentiles instead of averages, and the age of in-progress work compared with how long similar work usually takes.
Follow a delivery question from signal to cause
The reports are designed to be read together. Two common paths through them:
- Why did we miss the sprint? Start with carryover by delivery stage, check how it is calculated in the carryover methodology, look for the stage where work accumulates with bottleneck analysis, then plan the next sprint with sprint intelligence.
- Is work bouncing back from QA? Review requirements quality, see how signals are detected in the requirements methodology, and connect them to delivery risk in why sprints fail.
Evidence you can inspect, not a score to trust
Every conclusion is labelled observed, inferred or unavailable depending on what your export can support. A standard Jira CSV has no status history, so VeloWise says when a question needs more data instead of drawing a convincing but invented chart. The methodology documents every formula and threshold, and status names are mapped to delivery stages you can review and change.
Your data, your choice of storage
Jira CSVs are parsed and analyzed in your browser. Signed out, datasets stay in browser storage. With an organization project, imports are also stored for its members, protected by database-level access rules. Read the privacy model, security and organization access and data storage and deletion pages for the details.
Learn the metrics first
The engineering delivery guides explain each metric, how to calculate it from Jira and what not to conclude from it — useful whether or not you use VeloWise.
Start with a sample or your own export
Create a free account, set up an organization and project, and import a Jira CSV. You can explore the synthetic sample project first.
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