Trainloop is an agentic AI QA automation platform that autonomously generates, executes, and maintains tests for web and mobile applications.

How is Trainloop different from traditional QA automation tools?

Unlike script-based or AI-assisted tools, Trainloop uses fully autonomous AI agents to manage the entire QA lifecycle without manual test maintenance.

How is Trainloop 5x cheaper than legacy tools?

By using agentic reasoning to optimize execution paths and token usage, and eliminating the need for manual script maintenance, we reduce total cost of ownership by up to 80%.

Does Trainloop support web and native mobile testing?

Yes. Trainloop supports modern web applications as well as native Android and iOS mobile apps using unified agentic AI logic.

Is Trainloop suitable for startups and enterprises?

Yes. Trainloop is designed to scale from early-stage startups to large enterprise QA environments with features like SOC 2 compliance and VPC deployment.

What does agentic AI mean in QA testing?

Agentic AI refers to autonomous systems that can independently plan, execute, adapt, and optimize testing activities without human intervention or manual scripting.

Trainloop vs. Qualgent: Navigating the Autonomous Mobile Frontier

Qualgent and Trainloop represent the cutting edge of the Y-Combinator AI ecosystem. Both are solving the hardest problem in software: **Autonomous Mobile QA**.

Mobile Specialization vs. Platform Breadth

Qualgent V3 is a highly specialized "surgical" tool for mobile. It excels at deep mobile-specific interactions. Trainloop, conversely, provides a **Unified Agent Engine**. We believe that in 2026, quality is a cross-platform problem.

Autonomous Discovery: 7 Days to Value

One area where Trainloop stands out is our **7-Day Personalized Pilot**. We don't ask you to write "prompts" or "steps." Our agents spend 7 days exploring your production environment and deliver a fully functional, self-healing test suite by day 8.

The "Agentic Memory" Advantage

By using persistent memory, Trainloop agents learn from every failure. If a test fails in the CI, the agent analyzes *why* and automatically suggests the fix to your developers before they even check the logs.