Drizz Desktop eliminates the instability and overhead of selector-based mobile automation by enabling engineers to author and validate tests locally using Vision-AI-driven interactions. It reduces flakiness, accelerates debugging, and shortens the feedback loop before tests reach CI or large-scale cloud execution.

Drizz Desktop directly interfaces with Android Debug Bridge (ADB), iOS simulators, and supported device clouds. No agents or SDKs need to be embedded into the app - Drizz uses screen streams, Vision AI interpretation, and standardized interaction channels to execute commands deterministically.

By catching UI regressions, unstable steps, and structural gaps before the test suite reaches CI, Drizz Desktop prevents false negatives and expensive pipeline reruns. Engineers validate flows locally with screenshots, timings, and logs identical to cloud execution, ensuring consistency across environments.

Drizz Desktop is a local runtime that handles authoring, device connection, Vision AI interpretation, and synchronous execution.

Drizz Cloud is a distributed execution fabric optimized for parallelization, orchestration, access control, artifacts, and CI integrations.

Both share the same execution engine, ensuring parity between local and cloud runs.

No. Drizz abstracts UI identification entirely. The Vision Engine interprets the rendered screen, classifies actionable components, and executes commands without requiring selectors, element IDs, or platform-specific locators.

Every step captures:

• Before/after screens
• System state
• Execution timestamps
• Vision inference context
• Deterministic actions

This creates consistent reproducibility across device types, OS versions, and screen densities

Yes. Test files are simple text-based instructions that commit cleanly into Git repositories. Engineers can branch, diff, and review test logic just like application code.

Parallelism is bound by available devices/emulators connected to the engineer’s machine. Cloud execution removes this constraint entirely by horizontally scaling across device pools.

It operates alongside the IDE:

• Author flows while developing features
• Validate UI behavior across devices locally
• Commit test logic into version control
• Trigger distributed execution in CI via Drizz Cloud


This mirrors modern development practices while eliminating brittle automation frameworks.

The execution engine, Vision AI model, interaction pipeline, and step lifecycle are identical across both surfaces. Only the runtime environment differs, ensuring operational consistency between local validation and CI-scale execution.