---
title: Drizz Bags $2.7m for Vision AI To Fix Mobile App QA Bottlenecks – Will It Stick?
description: AI accelerates code yet mobile app QA lags behind. Vision-based testing tools like Drizz offer faster, script-free quality assurance for enterprises
author: Darie Nani (Editor-in-Chief)
date: 2025-07-28T11:05:00.000Z
updated: 2026-02-26T18:02:16.008Z
canonical: https://www.sovereignmagazine.com/article/drizz-bags-2-7m-for-vision-ai-to-fix-mobile-app-qa-bottlenecks-will-it-stick
image: https://cdn.nanimediahouse.com/Drizz-Founders-L-R-Partha-Mohanty-Yash-Varyani-Asad-Abrar.webp
categories: Artificial Intelligence
content_type: Spotlight
region: United States
publication: Sovereign Magazine
about:
  - type: Organization
    name: Drizz
    description: Drizz is a Vision AI mobile application testing agent, built for the speed and complexity of AI-powered app development. Drizz’s proprietary multimodal engine understands screen layouts, UI flows, and dynamic elements like a human would—enabling smarter, more stable test execution. Test scenarios can be authored using simple English prompts, allowing even non-technical stakeholders to contribute without writing code.
    url: http://www.drizz.dev/
    sameAs:
      - https://www.linkedin.com/company/drizz-dev/
---

AI is rewriting code at breakneck speed, yet mobile app testing remains painfully slow. Whilst development teams ship features faster than ever, quality assurance still relies on brittle scripts that break with every interface change, costing enterprises time and confidence. [23% of IT budgets now go to QA testing](https://www.gminsights.com/industry-analysis/software-testing-market), yet [70% of software projects still exceed budgets by 27%](https://www.testdevlab.com/blog/controlling-software-development-costs) due to testing inefficiencies.

Now [Drizz](http://www.drizz.dev/), a startup founded by engineers from Amazon, Coinbase and Gojek, claims it can cut mobile testing from days to minutes using Vision AI that requires no coding. The company raised $2.7 million in seed funding led by Stellaris Venture Partners and Shastra VC, with participation from Anuj Rathi and Vaibhav Domkundwar, betting that natural language prompts can finally solve QA’s most persistent bottlenecks.

## Pain Points That Sparked a Solution

The founders know testing frustrations firsthand. Asad Abrar, who served as a product manager at Coinbase before co-founding Drizz, watched development teams struggle with constant script maintenance. ‘During my time as a product manager at Coinbase, locator-based tests broke with every UI shift, turning QA into a bottleneck,’ Abrar said.

Traditional tools like [Appium and Selenium](https://testrigor.com/blog/why-appium-sucks-for-end-to-end-tests-in-2024/) require extensive coding expertise and suffer from high maintenance overhead. Scripts depend on fragile XPath selectors and accessibility IDs that break whenever interfaces change – precisely what happens most frequently in mobile development.

Partha Mohanty, Drizz’s co-founder and chief product officer, previously worked at Amazon and Gojek. Yash Varyani, the company’s CTO, rounds out a founding team that collectively witnessed these QA pain points across some of tech’s most demanding environments.

## Vision Over Code

Drizz’s approach eliminates traditional script dependencies entirely. Instead of writing code that hunts for specific UI elements, teams write test instructions in plain English. The platform’s AI evaluates apps visually – mimicking how actual users interact with interfaces – rather than relying on underlying code structures.

‘Drizz’s multimodal engine understands the screen context and layout, even when elements are constantly changing,’ said Varyani. ‘Where traditional testing may break, Drizz remains stable and flags bugs with detailed log intelligence that pinpoints the root cause.’

The company reports impressive metrics from early deployments: over 97% test accuracy and a 10x reduction in test creation time. Teams can run the same test suite across iOS and Android without maintaining separate code bases, addressing one of mobile QA’s most expensive requirements.

## Enterprise Stakes Get Higher

For enterprise buyers, these efficiency gains matter more than ever. [Quality assurance represents roughly 23% of IT spending](https://www.statista.com/statistics/500641/worldwide-qa-budget-allocation-as-percent-it-spend/), yet many teams still struggle to keep testing pace with development cycles accelerated by AI-generated code.

Drizz integrates with CI/CD pipelines and supports real device cloud testing – table stakes for enterprise adoption. More significantly, the platform allows non-technical stakeholders to contribute test scenarios without coding knowledge, potentially expanding QA participation beyond traditional engineering roles.

The startup already works with multiple unnamed unicorns and reports strong engagement: users spend an average of 15 hours weekly writing and executing test cases. [Similar AI automation tools](https://www.sovereignmagazine.com/article/ai-agents-take-centre-stage-logicflo-s-2-7m-seed-backs-human-guided-automation-in-pharma) are finding traction across industries, suggesting teams are finding genuine utility rather than just experimenting with new tools.

## Challenging Entrenched Methods

Drizz faces established competition from [traditional frameworks like Selenium and Appium](https://www.headspin.io/blog/best-mobile-app-testing-frameworks-for-android-ios), despite their known maintenance burdens. Many enterprise teams have invested heavily in existing test infrastructure, creating switching costs that extend beyond simple tool replacement.

The competitive arena may be shifting. [AI-augmented testing tools](https://www.gartner.com/reviews/market/ai-augmented-software-testing-tools) represent a growing category, with vendors like Katalon, Leapwork and others incorporating similar automation capabilities. The question becomes whether Drizz’s fastest-vision approach provides sufficient differentiation to capture meaningful market share.

Vision-based testing does offer potential advantages over script-based alternatives. Traditional tools require constant maintenance as interfaces evolve, whilst visual recognition can adapt to UI changes more gracefully. For mobile apps – where screen sizes, resolutions and interaction patterns vary dramatically across devices – this flexibility could prove decisive.

## Investor Confidence in QA Revolution

Stellaris Venture Partners, which led the funding round, sees wider implications in QA modernisation. Partner Alok Goyal explained the firm’s perspective: ‘AI is fundamentally changing how software is built, tested and deployed. In an era where more software needs to be shipped even faster than ever, software quality has become the biggest bottleneck.’

[Stellaris’s track record](https://www.bii.co.uk/en/our-impact/fund/stellaris-venture-partners-ii-investment-01/) in enterprise software and infrastructure investments supports this thesis. The firm has built its reputation backing companies that address fundamental development inefficiencies, making Drizz’s QA focus a natural fit for their portfolio approach.

Shastra VC’s participation adds weight to the investment, suggesting multiple experienced investors see potential in displacing traditional testing approaches.

## Unanswered Questions Remain

Despite promising early metrics, Drizz faces several unproven challenges. Vision-based testing must handle edge cases and complex user flows that traditional script-based tools already manage reliably. [Enterprise teams need confidence](https://www.sovereignmagazine.com/article/bigeye-bets-on-ai-trust-platforms-as-mohamed-k-alimi-joins-to-build-agent-oversight-tools) that new platforms won’t introduce different failure modes whilst solving existing ones.

Switching costs present another hurdle. Teams with extensive Selenium or Appium investments face not just tool replacement but potentially significant test suite rewriting. [AI testing tools can occasionally generate irrelevant outputs](https://www.rainforestqa.com/blog/ai-testing-tools), requiring human oversight that may offset some promised efficiency gains.

The competitive response from established players remains unclear. If vision-based approaches prove superior, incumbent tool makers could rapidly incorporate similar capabilities, potentially neutralising Drizz’s fast-mover advantage.

## The QA Catching-Up Moment

Drizz’s $2.7 million bet ultimately tests whether mobile QA can finally match the speed of modern development cycles. AI has already accelerated code generation, but testing remains frustratingly manual and error-prone for many teams.

For investors, the appeal lies not just in Drizz’s specific product but in the potential for any solution that genuinely solves QA bottlenecks. [Enterprise software budgets](https://www.sovereignmagazine.com/article/the-billion-dollar-phone-problem-the-hard-numbers-behind-ai-agents) face constant scrutiny while development velocity becomes increasingly competitive, meaning tools that deliver both speed and reliability could command significant premiums.

Whether Drizz becomes the platform that displaces decades of script-based testing or simply another layer in an increasingly complex QA stack will depend on execution in real-world enterprise environments. The early signs suggest strong user engagement, but the true test comes when teams bet their release cycles on vision over code.

**About Drizz**

Drizz is a Vision AI mobile application testing agent, built for the speed and complexity of AI-powered app development. Drizz’s proprietary multimodal engine understands screen layouts, UI flows, and dynamic elements like a human would—enabling smarter, more stable test execution. Test scenarios can be authored using simple English prompts, allowing even non-technical stakeholders to contribute without writing code.

[Website](http://www.drizz.dev/)