Cut test suite time by 90%

MEET ORCHARD

The AI-native testing platform that helps high-velocity teams ship features faster without sacrificing quality.

Agentic Self-Healing

Orchard’s agentic AI adapts to interface changes instantly, maintaining reliable tests without manual updates.

Teams keep momentum, reduce maintenance overhead, and prevent brittle suites from slowing delivery.
  • AI analyses intent, not coordinates, ensuring tests keep working even when elements move or layouts shift across releases.
  • Automatically repairs broken selectors and flows, removing the need for engineers to spend hours fixing outdated or fragile tests.
  • Continuously learns from execution history to strengthen stability, cut flakiness, and provide predictable behaviour across fast-changing codebases.

Natural Language Authoring

Write tests in plain English and let Orchard convert them into reliable automated flows. PMs, QAs, and engineers can author complete suites in minutes without scripting knowledge.

  • Understands everyday language to generate precise, executable steps, reducing the need for technical expertise or complex frameworks.
  • Captures intent clearly, producing consistent tests that reflect actual user journeys rather than brittle, step-by-step scripting.
  • Enables rapid authoring of large suites, helping teams move from idea to execution within minutes, not days.

Massive Parallel Execution

Orchard executes hundreds of tests at once, compressing multi-hour suites into minutes and enabling rapid release cycles without compromising accuracy.

  • Runs hundreds of concurrent tests to eliminate bottlenecks in your deployment pipeline.
  • Delivers consistent results at high load, ensuring stable performance under demanding workloads.
  • Reduces suite execution from hours to minutes, enabling faster iteration across teams.

Helping teams adopt AI-native testing with clarity, speed, and dependable support.

Built by Seedling Labs to bring speed, accuracy, and AI-powered testing to modern engineering teams.

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