Evaluate dVI
Evaluate dVI on one real regression.
Start with one real regression, agreed success criteria, and a fixed decision point inside your environment.
A focused product evaluation
One regression. One proof bar. One decision.
- 01
Choose the regression
Select a verification problem where closure delay is visible, recurring, and worth measuring.
- 02
Connect the existing flow
Map the source, simulator, regression evidence, deployment boundary, and optional Jira connection without replacing the working stack.
- 03
Agree the proof bar
Define reproduction, targeted verification, broader checks, change boundaries, limits, approvals, and acceptable non-fix outcomes up front.
- 04
Run the closure loop
Take the real failure through analysis, controlled change, approved reruns, and a customer-visible outcome.
- 05
Make the decision
Review evidence quality, operational fit, usage, blockers, and team value at the agreed decision point.
Agreed success criteria
Decide from agreed evidence.
We define the result before the evaluation starts and return to the same criteria at the fixed decision point.
- A real baseline failure is reproduced through the agreed project flow.
- The original source remains unchanged and the proposed change is exact and inspectable.
- The agreed targeted and broader verification checks run against that exact change.
- The result is a verified outcome or a precise, actionable blocker or handoff.
- The team can inspect decisions, verification, usage, controls, and the stopping reason.
- The review ends with an explicit expand, adjust, or stop decision.
A good first evaluation
Real enough to matter. Bounded enough to prove.
You own a regression with costly or repetitive closure work.
You can provide an existing source and regression flow rather than a synthetic demo.
You need deployment and execution to stay under customer control.
You can agree the proof bar and supported change boundary up front.
You want a measured decision, not an open-ended evaluation.
Product working session
Start with the regression you actually need to close.
We will discuss the current flow, deployment boundary, proof criteria, and the smallest credible evaluation. No public sandbox is required.