Adept AI vs Stably AI

Side-by-side comparison of AI visibility scores, market position, and capabilities

Adept AI

EmergingAI Infra

AI Agents

Adept AI raised $415M to pioneer computer-use AI agents; its core research and agent team moved to Amazon in 2024 in a landmark talent acquisition while the company continues developing ACT-1 for enterprise automation.

About

Adept AI was founded in 2022 by a team of former OpenAI, DeepMind, and Google Brain researchers to build AI that can take actions on computers — navigating software interfaces, filling forms, and executing multi-step workflows in any application. Its ACT-1 model demonstrated the ability to control web browsers and desktop applications through natural language instructions, pioneering the computer-use agent paradigm that Anthropic later commercialized with Claude's computer use feature.

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Stably AI

LeaderInfrastructure

IT Operations

SF YC AI test automation at $1M ARR Dec 2024 with 5 employees; ex-Google/Uber founders with self-healing tests that auto-repair when UI changes helping OpenArt scale to $16M ARR competing with Mabl for zero-flakiness CI testing.

AI VisibilityBeta
Overall Score
B78
Category Rank
#5 of 68
AI Consensus
71%
Trend
stable
Per Platform
ChatGPT
89
Perplexity
78
Gemini
88

About

Stably AI is a San Francisco-based AI test automation platform — backed by Y Combinator — reaching $1 million in annual revenue in December 2024 with a 5-person team — providing engineering teams with an AI platform that auto-generates, runs, and maintains end-to-end tests in CI/CD pipelines with zero-flakiness guarantees and self-healing capabilities that automatically repair tests when UIs change, replacing the brittle Playwright and Cypress test suites that break with every UI update. Founded in 2023 by ex-Google Chrome infrastructure engineer Jinjing Liang (CEO) and ex-Uber Safety ML engineer Neil Parker (CTO), Stably enables customers like OpenArt (which scaled to $16M ARR with a 10-person engineering team using Stably) to achieve test coverage without dedicated QA engineers.

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