# Autosana: self-healing end-to-end testing for mobile and web releases

Canonical: https://mudpie.ai/companies/autosana/
Breadcrumb: [Home](https://mudpie.ai/) / [Companies](https://mudpie.ai/companies/) / [Autosana: self-healing end-to-end testing for mobile and web releases](https://mudpie.ai/companies/autosana/)
Author: Ali Abouelatta (https://mudpie.ai/authors/ali-abouelatta/)
Published: 2026-09-19
Updated: 2026-09-19
Research type: Company profile
Method: Company and accelerator sources checked 2026-09-19. Product claims are attributed to their sources; this is research, not a hands-on product trial.

Autosana is an agentic end-to-end testing layer for mobile and web products whose code is changing faster than their test suite. The decision is whether self-healing, natural-language flows and PR-level video proof can replace enough brittle maintenance to justify adding another testing surface.

## What it does

Autosana says teams can describe flows in natural language, run them across iOS, Android, and web, and let agents create or update tests from code diffs. Its docs list local and cloud runs, CI/CD integration, code-managed flows, scheduled automations, screenshots, and self-healing behavior without XPath or CSS selectors ([Autosana docs](https://docs.autosana.ai/introduction); [YC launch](https://www.ycombinator.com/launches/Q1E-autosana-2-0-the-e2e-validation-layer-for-mobile-web-apps)).

| Fact | What the public sources say |
| --- | --- |
| Buyer | Product and engineering teams shipping mobile and web apps |
| Test surface | iOS, Android, mobile web, and desktop web |
| Workflow | Natural-language flows, code-diff test updates, CI/CD, screenshots, and video proof |
| Public case study | Autosana says Ziina automated 69% of its E2E suite and cut regression-testing time by 90% |
| Founders | Yuvan Sundrani and Jason Steinberg |

## Why it fits

The core advantage is keeping validation close to the coding agent. Instead of asking a team to maintain selectors after every UI change, Autosana aims to let the agent see the app, run a flow, and show the result in a pull request. That is particularly relevant for flows that are awkward to script—mobile gestures, OAuth, magic links, in-app browsers, and cross-app authentication ([YC launch](https://www.ycombinator.com/launches/Q1E-autosana-2-0-the-e2e-validation-layer-for-mobile-web-apps)).

The Ziina figures are from Autosana's own case study, not an independent benchmark ([Autosana case study](https://autosana.ai/blogs/ziina-case-study)). A team should replay a representative regression set and resolve device coverage, test data, secrets, flaky environments, app-store constraints, video retention, and how a failing visual assertion is triaged. The public sources do not show a price.

The founders have lived in fast-moving product teams. YC describes Sundrani as an early engineer and later CPO at a martech startup and engineer at an AI-therapy company; Steinberg worked on founding teams of mobile-first startups ([YC company profile](https://www.ycombinator.com/companies/autosana)). That background fits the pain of mobile QA, but does not prove every app can be tested without setup.

Short version: Autosana is worth trying when regression maintenance, not test-writing syntax, is the bottleneck. Keep deterministic checks and release judgment where a vision-based agent cannot yet give enough confidence.

## Sources checked — 2026-09-19

- [YC company profile](https://www.ycombinator.com/companies/autosana)
- [Autosana docs](https://docs.autosana.ai/introduction)
- [Autosana YC launch](https://www.ycombinator.com/launches/Q1E-autosana-2-0-the-e2e-validation-layer-for-mobile-web-apps)
- [Autosana Ziina case study](https://autosana.ai/blogs/ziina-case-study)

## Cohort context

Autosana is listed in Summer 2025. In our 2026-09-18 directory snapshot, 112 of 166 listed companies in that cohort have YC’s primary industry label B2B (67.5%). This is a current-directory comparison, not an original intake count or a performance ranking. [Nine-cohort dataset](https://mudpie.ai/research/yc-cohorts-2026-09-19.json).

## Public website snapshot

Observed 2026-09-19T16:17:56.964Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.

| Signal | Homepage observation |
| --- | --- |
| Product description metadata | Observed |
| Canonical link | Not observed in this response |
| H1 or H2 heading | Observed |
| Typed structured data | Observed |
| Docs/developer link | Observed |
| Pricing link | Not observed in this response |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |

[Public observations](https://mudpie.ai/research/yc-homepage-links-2026-09-19.json) · [Collection method](https://mudpie.ai/research/yc-homepage-methods/README.md). Missing links here do not establish that a capability or file is absent elsewhere.


## Author disclosure

I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.