Funding Boost for a New Kind of QA Platform
Holmes has secured €1.1 million in pre-seed funding to build an autonomous QA platform tailored for teams shipping software at AI speed. The round was led by Syndicate One, joined by founders and leaders behind companies such as Aikido and Showpad, along with investment funds NewSchool.vc, RDY Capital, and 100IN. The startup is founded by Robin Praet, Robbrecht Delrue, and Sofie Buyse, entrepreneurs with prior exits in legal tech and hospitality software. Their thesis is straightforward: as AI coding tools accelerate feature development, quality assurance has become the new bottleneck. Instead of expanding traditional QA headcount, Holmes wants to embed software testing automation directly into the development lifecycle, enabling continuous testing that scales with product complexity. The fresh capital will fuel product development, team expansion, and a broader rollout beyond the company’s current group of design partners.
From Manual Scripts to Autonomous QA
Holmes positions itself as an autonomous QA platform that shifts testing away from manually written scripts toward behavior-driven automation. Rather than relying on engineers and QA testers to continually author and update test cases, Holmes observes how a web application is actually used. It learns core flows such as sign-up, login, checkout, search, navigation, and form completion, then generates tests that continuously verify these journeys as the product evolves. Five specialised AI agents handle different aspects of software testing automation, focusing on happy paths, edge cases, responsive layouts, accessibility, and error recovery. Because the system runs inside the tools development teams already use, tests can execute in the background without new manual workflows. The goal is to catch issues before users encounter them, validating not only whether the code compiles or appears correct, but whether the product behaves as expected in real-world usage.
Tackling the Testing Bottleneck in AI-Driven Development
AI-driven development has dramatically accelerated how quickly code is produced, but QA processes remain largely human-bound. Holmes argues this mismatch forces teams into an uncomfortable trade-off: either slow down to maintain manual tests or push ahead and risk bugs slipping into production. Many companies delay building dedicated QA teams, leaving developers and product managers to shoulder testing tasks in addition to their core responsibilities. This often fragments ownership of quality and introduces gaps when teams are under pressure. By enabling continuous testing without manual upkeep, Holmes aims to restore confidence in fast release cycles. Its platform evaluates user-facing flows end-to-end, ensuring that changes driven by AI-assisted coding tools are continuously validated. In practice, this means teams can lean into AI acceleration while maintaining a safety net that checks whether features still work for real users, not just in theory.
What Holmes’s Approach Means for QA Teams
Holmes’s model signals a shift in how QA work may be organised rather than eliminated. For early-stage software teams, the platform offers a way to introduce structured continuous testing before a full QA department exists, reducing reliance on ad hoc manual checks. For more mature organisations, autonomous testing can offload repetitive regression work, allowing specialist QA professionals to focus on exploratory testing, complex edge cases, and broader quality strategy. The platform’s emphasis on user journeys, accessibility, and error recovery suggests QA will increasingly be about supervising and refining AI-driven test coverage instead of crafting every case by hand. As Holmes works with dozens of design partners and advisors from established tech companies, its trajectory will be a bellwether for how autonomous QA platforms reshape collaboration between developers, product managers, and QA teams in an AI-first development environment.