Drizz

Company profile:

Startup name: Drizz

Tagline: Vision AI mobile app testing. Write tests in plain English, run on real devices, no selectors needed.

Elevator Pitch: Mobile app testing is broken. Most teams rely on selector-based scripts that break every time the UI changes, forcing QA teams to spend more time fixing tests than actually testing. Drizz fixes this with Vision AI — instead of hunting for elements in code, it reads the screen the way a human tester would. QA teams write test steps in plain English, and Drizz executes them on real iOS and Android devices. When the interface changes, tests adapt instead of failing, cutting out the constant maintenance that eats up QA time. Teams using Drizz report writing tests roughly 10x faster than with traditional tools like Appium, with far less flakiness. It plugs directly into CI/CD pipelines, so testing keeps up with how fast mobile teams actually ship today.

If you need a tighter, one-breath version:

Drizz uses Vision AI to test mobile apps the way a human would — reading the screen instead of hunting for selectors. QA teams write tests in plain English, run them on real iOS and Android devices, and stop losing time to tests that break every time the UI changes.

Target Market: Mobile app development teams, QA engineers, software testers, and enterprises seeking AI-powered test automation for iOS and Android apps.

How will you make money?: null

How much capital have you raised?: 250-500k

Website: https://www.drizz.dev

City/Country: San Francisco

AI-assisted summary:

Drizz is a mobile-app test automation startup that lets teams define tests in plain English, execute them on real iOS and Android devices, and use vision-based AI to adapt tests when the user interface changes. (Source: https://www.drizz.dev/)

The company matters because it is targeting a persistent release-engineering problem: mobile teams need reliable regression coverage, but selector-based test automation can be slow to author and costly to maintain as apps evolve. (Source: https://www.drizz.dev/)

The problem and target users

Drizz is aimed at mobile QA teams, developers, engineering leaders, and teams moving from manual or partially automated testing toward broader automated coverage. (Source: https://www.drizz.dev/)

Its core premise is that conventional locator- and selector-based automation can fail when UI elements change, creating flaky runs, maintenance work, and gaps in testing important flows such as login, payment, and checkout. (Source: https://www.drizz.dev/)

The wider market opportunity is tied to faster software delivery and a growing need to test mobile applications across devices and operating systems; Forbes reported in July 2025 that Drizz entered a competitive market that includes established open-source tooling such as Appium and newer automation vendors. (Source: https://www.forbes.com/sites/davidprosser/2025/07/28/start-ups-eye-the-opportunity-in-ai-software-testing-market/)

Product and solution

Drizz’s product uses a vision-based approach to mobile testing: users describe an intended flow in natural language, and the platform creates and executes tests rather than requiring them to write traditional automation scripts. (Source: https://www.drizz.dev/)

The company says its system runs tests on real iPhones and Android devices, supports both Android and iOS applications, and enables users to review the steps and proposed changes made by the AI. (Source: https://www.drizz.dev/)

• Plain-English test authoring is designed to let users specify app behaviour without writing code. (Source: https://www.drizz.dev/about-us)
• Vision AI evaluates the app visually instead of relying on underlying UI locators such as XPath or accessibility IDs, according to the company’s launch coverage. (Source: https://siliconangle.com/2025/07/28/drizz-launches-2-7m-provide-vision-based-testing-ai-mobile-apps/)
• Drizz says tests can self-heal when UI changes occur and can be reused across iOS and Android workflows. (Source: https://www.drizz.dev/)
• The platform also advertises centralized management of apps, builds, suites, devices, environments, and test-run history, plus CI/CD integration and debugging artifacts including screenshots, logs, and screen state. (Source: https://www.drizz.dev/)
• Its listed capabilities include end-to-end testing, UI and API coverage, accessibility testing, and real-device execution. (Source: https://www.drizz.dev/)

Business model, pricing signals, and traction

Drizz presents a SaaS-style commercial model with a free trial, pay-as-you-go test runs, a team plan, and an enterprise tier. (Source: https://www.drizz.dev/)

The free trial includes 50 test runs, while the pay-as-you-go offering is positioned for solo developers or teams with variable testing needs; public pricing amounts were not listed on the company’s main pricing section at the time of review. (Source: https://www.drizz.dev/)

The enterprise offer includes signals typical of higher-value, security-sensitive sales, including on-premises or VPC deployments, unlimited test runs, custom SLAs, SSO/SAML, dedicated account management, and custom workflows or integrations. (Source: https://www.drizz.dev/)

Drizz announced a $2.7 million seed round on July 28, 2025, led by Stellaris Venture Partners and Shastra VC, with participation from Anuj Rathi and Vaibhav Domkundwar. (Source: https://siliconangle.com/2025/07/28/drizz-launches-2-7m-provide-vision-based-testing-ai-mobile-apps/)

At the time of that announcement, Forbes reported that Drizz had signed roughly a dozen customers and was emerging from stealth; this is a historical traction snapshot rather than a current customer count. (Source: https://www.forbes.com/sites/davidprosser/2025/07/28/start-ups-eye-the-opportunity-in-ai-software-testing-market/)

Expert take

Drizz is positioning itself around a practical wedge: reducing the maintenance burden of mobile UI testing while making automation more accessible to people who can describe product behaviour but do not write test code.

Its strengths are a focused mobile use case, real-device execution, and a commercial ladder that can serve individual builders through regulated enterprise buyers. The central diligence question is whether its claimed reliability and self-healing behaviour remain consistent across highly dynamic apps, complex edge cases, and production-scale release pipelines.

Note: Information based on publicly available sources at the time of writing, and summarized by AI.