While everyone's talking about AI coding, the real money might be in fixing what gets built. Momentic, an AI-powered software testing startup, just closed a $15 million Series A led by Standard Capital with Dropbox Ventures joining the round. The funding signals growing investor confidence that AI can solve one of development's most persistent headaches - making sure code actually works before it ships to users.
The unsexy side of software development just got a major cash injection. Momentic announced Monday it raised $15 million in Series A funding led by Standard Capital, with Dropbox Ventures participating alongside existing investors Y Combinator, FCVC, Transpose Platform and Karman Ventures. The round builds on a $3.7 million seed announced in March, bringing total funding to nearly $19 million.
While product demos grab headlines, co-founder Wei-Wei Wu knows the real work happens behind the scenes. "Testing has been the biggest pain point for every team I've ever worked with," Wu told TechCrunch. His solution? Let AI handle the tedious but critical work of quality assurance that keeps software running smoothly.
Momentic's pitch is deceptively simple - developers describe their critical user flows in plain English, and the company's AI automatically creates and runs the tests. "We help our customers make sure their product works," Wu explained. "They can describe their critical user flows in plain English and our AI will automate it." It's a stark contrast to existing open-source frameworks like Playwright and Selenium, which offer granular control but require significant technical expertise to implement effectively.
The approach is already resonating with major players. Momentic counts 2,600 users across companies including Notion, Xero, Bilt, Webflow, and Retool. Wu stayed tight-lipped about revenue figures, but said growth metrics were strong enough to convince investors. The scale becomes clear when you look at the numbers - Wu estimates Momentic automated over 200 million test steps in the past month alone, a volume that would be virtually impossible with traditional manual testing approaches.
