Autosana Is Replacing Your QA Team With a Prompt, and It Actually Works

The Macro: QA Testing Is a Tax on Every Engineering Team

Every mobile developer knows the drill. You ship a feature, the QA cycle takes three days, someone finds a regression on Android 13 that does not reproduce on Android 14, and the release slips. Multiply this by every sprint for the life of a product. QA is not a bottleneck because teams are bad at it. QA is a bottleneck because the entire approach is structurally broken.

The traditional options are not great. Manual QA is expensive and slow. Selenium and Appium scripts are brittle. Every time the UI changes, the selectors break, someone spends a morning fixing locators, and the whole thing feels like maintaining a second codebase that produces no user value. Playwright improved the developer experience for web testing, but mobile is still a mess. And cross-platform frameworks like React Native and Flutter add their own layer of testing complexity because the rendered output differs by platform.

There are companies trying to fix this. Testim (acquired by Tricentis) uses AI to stabilize tests. Mabl does visual regression testing. Applitools focuses on visual AI. Katalon offers a low-code test platform. But most of these tools still require you to think in terms of selectors, page objects, and assertion chains. They automated the execution of tests. They did not rethink what a test should be.

The real question is whether natural language can replace test scripts entirely. Not “write tests faster” but “describe what the app should do and let a machine figure out how to verify it.” That is a fundamentally different product, and it is what the next generation of QA tools is betting on.

The Micro: Robotics-Adjacent Founders Building for Mobile

Autosana was founded by Yuvan Sundrani and Jason Steinberg. Yuvan was Engineer number one at a martech startup that grew to seven figures in revenue, then took the same role at an AI-therapy startup. He has been the first engineer at multiple companies, which means he has lived through the pain of shipping mobile apps without adequate testing infrastructure. Jason worked on founding teams of multiple mobile-first startups. Both are builders who have shipped consumer products and felt the drag of manual QA firsthand.

They are a three-person team out of San Francisco, part of Y Combinator’s Summer 2025 batch. The product is live and the pitch is straightforward: write your test in plain English, and Autosana’s AI agents simulate real user interactions across iOS, Android, and web. No selectors. No framework-specific syntax. No test maintenance when you redesign a screen.

The “self-healing” piece is what separates this from a fancy test recorder. When your app’s UI changes, traditional tests break. Autosana’s agents re-evaluate the interface and adapt. If a button moves from the top of the screen to a bottom sheet, or if a label changes from “Submit” to “Confirm,” the agent figures it out. This is not hypothetical. Self-healing test tools have been promised before, but the combination of modern vision models and LLM reasoning makes it actually viable now in a way it was not two years ago.

The product integrates into CI/CD pipelines, which is table stakes but still important to mention. If your tests cannot run on every pull request, they are demos, not infrastructure. Autosana also includes session replay for debugging failures, which saves the usual back-and-forth of “what did the agent actually see when it failed?”

The framework-agnostic claim is a big deal. React Native, Flutter, Swift, Kotlin, web. One testing tool across all of them. If that works reliably, it eliminates the need for separate test suites per platform, which is where most mobile teams lose the most time.

Their estimate is 8+ engineering hours saved per deployment. I think that number is conservative for teams running comprehensive regression suites. The real savings come from not having to maintain the tests at all.

The Verdict

I think Autosana is attacking the right problem at the right time. The testing tools market has been waiting for AI to get good enough to actually replace scripted tests, and we are there now. Vision-language models can look at a screen and understand what they see. LLMs can interpret a natural language instruction and map it to a sequence of interactions. The technology stack finally supports the product vision.

The competitive risk is real but navigable. Testim has resources from Tricentis. Mabl has enterprise distribution. But both are iterating on the old paradigm. Autosana is building for a world where tests are described, not coded. That is a different product for a different buyer.

Thirty days, I want to see how the self-healing performs on real-world app updates. Not demo apps. Real apps with messy UIs and inconsistent design systems. Sixty days, the question is CI/CD reliability. Can Autosana run 200 tests per PR without flaking out more than a hand-written suite? Ninety days, I want to know if mobile-first companies are adopting this as their primary testing tool, not just a supplement. If teams start deleting their Appium suites because Autosana makes them redundant, this company wins. If Autosana ends up as “one more testing tool” alongside everything else, the value proposition gets diluted fast.