As AI coding assistants generate and refactor more of your application, failures pile up faster than any team can review by hand. Applitools shows exactly what changed at the DOM level, groups similar failures together, and updates hundreds of tests with one decision.
Know what broke and why before you've finished opening a ticket.
Clear a hundred flagged tests with the same decision you'd make for one.
Every result lands in the same place, so nothing gets reviewed twice or missed entirely.
Skip the visual guessing game
Click a visual difference and see the exact DOM and CSS rule change that caused it, instead of comparing screenshots side by side.
Built for a faster feedback loop
Developers get the specific line that changed, not just a screenshot that something looks different, so fixes happen in minutes.
Keeps pace with AI-generated commits
When an AI coding assistant touches a component, root cause analysis shows whether the change was intentional or a regression, without someone tracing it back through the commit history by hand.
Groups changes automatically
Applitools groups similar visual differences by component, environment, or team, instead of listing every affected test as a separate item to review.
One decision, hundreds of tests
Accept or reject a change once, and Applitools applies that decision across every matching instance in your suite.
Maintenance that scales with your commit rate
As AI-assisted development pushes more changes through your pipeline, automated maintenance keeps the backlog from growing alongside it.
Functional and visual results, together
See functional and visual test results in a single dashboard instead of piecing them together from separate tools.
Trends over time, not just pass and fail
Track results across releases to see where defects cluster and where coverage is thin.
A shared view for the whole team
Give developers, QA, and product managers the same picture of quality, so a conversation about risk starts from the same data, including how much of it is coming from AI-generated changes.
Every diagnosis traces back to an actual DOM and CSS change computed by the Deterministic Language Model, not a guess about what might have caused it.
Root cause analysis and automated maintenance both scale with commit volume, so triage doesn't get harder just because AI is writing more of the code.
Visual AI flags a change only when it would look different to a user, so analysis starts from a list of real issues, not rendering noise.
Applitools compares screenshots locally within your environment. App data and anything personal on screen is never used for training.
