Drizz is an automated testing platform for mobile developers that uses computer vision to navigate and validate iOS and Android applications. Instead of relying on traditional code-based selectors or brittle element IDs that often break during UI updates, the system interprets the mobile interface much like a human tester does. This allows QA teams to draft test cases in plain English, which the AI agent then translates into actionable steps across real physical devices. The platform handles the heavy lifting of mobile automation by offering both a desktop application for local script authoring and a cloud-based infrastructure for high-volume execution. Because the tool operates on the visual layer of the app rather than the underlying code, it significantly reduces the technical debt typically spent fixing broken tests after minor design changes. While this visual-first approach simplifies the workflow for non-technical stakeholders, teams must still ensure their natural language prompts are precise enough to handle complex conditional logic. Drizz serves as a practical alternative for organizations looking to move away from the high maintenance costs and fragility associated with legacy mobile testing frameworks.
Problem: Traditional automated tests break frequently when developers change element IDs or CSS classes.
Solution: Computer vision allows the AI to navigate the app by 'seeing' buttons and text like a human user would.
Example: A mobile app undergoes a major UI redesign, but the existing tests still pass because the 'Login' button looks similar.
Problem: Manual testers often lack the coding skills required to write complex Appium or Selenium scripts.
Solution: Users write test steps in plain English, which the AI then executes on real physical devices.
Example: A QA analyst writes 'Open the app and add a blue shirt to the cart,' and the AI handles the device interactions.
Problem: Maintaining separate test suites for iOS and Android creates significant overhead for small teams.
Solution: The platform supports unified test authoring that works across both major mobile operating systems.
Example: A startup tests their core checkout flow on both an iPhone and a Samsung Galaxy using the same plain-English script.
Target audience: Best for: QA Teams, Mobile Developers, App Development Agencies
Pricing: Free Trial · Categories: Developer Tools, Low-code/No-code, Productivity
Tags: AI, ai agent, developer tools, low-code/no-code, startup tools
Drizz automates mobile application testing across iOS and Android devices using computer vision. It visually navigates app interfaces, translates plain English instructions into automated test actions on real physical devices, and detects bugs. The platform supports local script authoring via a desktop app alongside cloud-based execution for large-scale testing runs.
Drizz uses computer vision to interact with mobile applications rather than relying on underlying code, CSS classes, or element IDs. By viewing UI elements such as buttons and text fields the way a human user does, the AI agent maintains test stability even after minor design adjustments or front-end code refactoring.
Drizz supports testing for both iOS and Android mobile applications. Teams can use unified plain English test descriptions to validate flows across both operating systems on real physical devices, avoiding the overhead of maintaining separate test frameworks or platform-specific test scripts for Apple and Google devices.
Yes, Drizz provides a free trial for users to evaluate its automated mobile testing capabilities. Development and QA teams can test the desktop authoring application, create plain English test cases, and explore computer vision-based execution on real devices before committing to a paid commercial subscription plan.
Drizz is designed for mobile developers, quality assurance teams, and app development agencies. It is particularly useful for teams looking to reduce the maintenance burden of brittle automated tests or those without deep programming experience in traditional mobile automation frameworks like Appium.