Problem

Modern product teams need to release mobile and web features quickly, but regression testing remains slow, manual, and dependent on brittle scripts that break as interfaces change. The resulting coverage gaps can let costly production bugs reach users, while engineering and QA teams spend time maintaining tests instead of shipping features. Autosana’s core use case is automated end-to-end validation of critical user journeys before release, particularly for consumer applications where a broken login, payment, trading, or onboarding flow can affect large numbers of users.

The economic pain is reinforced by the scale of the applications Autosana says it supports: its QA has been used for apps serving more than 100 million daily active users, and the company reports that revenue has grown by more than 100% per month on average since launch. The product is therefore aimed not merely at convenience, but at reducing the labor and release risk associated with testing high-traffic applications.

Product / Service

Autosana is an agentic quality-assurance platform that lets teams create end-to-end tests in natural language, without writing code. Its AI agents create, update, and run tests across iOS, Android, and web applications, while self-healing tests adapt to user-interface changes. The intended workflow is to describe a user journey in plain English, have the agent execute it like a human, and continuously maintain the test as the product evolves.

The benefit is a testing layer that closes the loop between coding agents and real application behavior: teams can ship faster with more confidence, replace some manual QA and brittle scripted maintenance, and catch regressions before they become production incidents. Autosana presents the service as a managed platform accessed through its product and demo-led commercial model, rather than as a consulting engagement.

Market

Autosana competes in AI-augmented software testing, specifically agentic end-to-end and mobile application testing. Its competitive set spans newer AI testing products such as Panto AI, Octomind, QA.tech, Testpine, and DogQ, as well as established or adjacent automation platforms including Maestro, Sofy, Kobiton, TestRigor, Tricentis Testim, and ACCELQ. The differentiation implied by Autosana’s positioning is broad coverage across mobile and web, natural-language test creation, and automatic test maintenance rather than conventional fixed scripts alone.

Autosana is not pre-revenue. It was founded in 2025, is backed by Y Combinator, and reported a $3.2 million funding round in February 2026; another company profile reports $3.7 million in total funding. The company says it serves apps with more than 100 million daily active users and has grown revenue more than 100% month over month on average since launch, while its website displays customer or partner logos including Phantom, Polymarket, Mercor, Locket, Fable, Photoroom, Ziina, and Audiomack. These figures and logos indicate early commercial traction, although the available evidence does not establish audited revenue, customer counts, or retention.