Why Traditional QA Can’t Keep Up With AI-Speed Development
Software is being written faster than ever as developers lean on AI coding assistants, but quality assurance hasn’t kept pace. Most teams still rely on manually written and maintained tests, or on product managers and engineers clicking through user flows before each release. That workflow doesn’t scale when code changes multiple times a day. The result is a painful trade-off: either slow down releases to test thoroughly, or ship quickly and risk bugs slipping into production. Autonomous software testing platforms address this gap by shifting QA from a manual, script-driven activity to an always-on, AI-powered safety net. Instead of checking whether the code looks correct in isolation, they focus on whether the product behaves correctly in real-world user journeys—sign-up, login, checkout, search, and more—no matter how frequently those journeys change.
Holmes Raises €1.1M to Build an Autonomous QA Platform
Holmes, a new entrant in AI QA automation, has secured a €1.1 million pre-seed round to reinvent how teams test software. The funding was led by Syndicate One, alongside experienced founders and investors from the broader technology ecosystem. Holmes is built by a founding team with prior exits and deep product experience, and they are positioning the company squarely at teams shipping at AI speed. Instead of asking engineers to constantly update brittle test scripts, Holmes embeds itself in the tools teams already use and learns how people actually use the product. From there, it automatically constructs and maintains tests around those critical flows. The company is already collaborating with dozens of design partners, using that early access to refine its continuous testing platform before a wider rollout. The new capital will fuel product development and an expanded engineering team.
How Bug Detection AI Learns and Tests Real User Journeys
Holmes’ approach to autonomous software testing centres on understanding complete user journeys rather than isolated features. The platform observes how users navigate a web application—from sign-up and login to navigation, forms, search, and checkout—and builds an internal model of those flows. Bug detection AI agents then generate and run tests continuously, verifying both common ‘happy paths’ and edge cases. Holmes uses specialised AI agents to cover key dimensions: core success flows, unusual user behaviour, responsive layouts across devices, accessibility compliance, and error recovery when something goes wrong. Because these tests are generated from live usage patterns, they evolve as the product evolves, reducing the need for manual test maintenance. This continuous, AI-driven coverage helps surface regressions and UX issues before they reach users, increasing confidence that each release will behave as expected in production.
From Manual Bottleneck to Continuous Testing Platform
In many teams, QA is recognised as critical but rarely has a clear owner. Dedicated testers can be expensive and hard to hire, so testing often lands on already overloaded developers and product managers. As products and engineering teams grow, this ad hoc approach turns into a bottleneck that slows releases and limits experimentation. Autonomous QA platforms like Holmes are designed for that inflection point. By running tests continuously in the background and updating them automatically as interfaces change, they effectively convert QA from a sporadic, manual checkpoint into a continuous testing platform. Teams can iterate faster—shipping new features, redesigns, or AI-powered capabilities—without repeatedly stopping to rebuild test suites. The promise is a new baseline: high-velocity development that still catches bugs early, protects the user experience, and reduces the operational risk of moving fast.