New York, NY – Spur, an artificial intelligence-powered quality assurance (QA) company, recently announced significant milestones achieved by its AI agents over the past year, including saving customers over $300,000 by preventing a critical airline pricing bug and reclaiming hundreds of QA hours. The announcement was made via a tweet, celebrating a "Spur Wrapped Edition" on "Be Nice to Bugs Day."
Spur, founded in 2024 by Yale graduates Sneha Sivakumar and Anushka Nijhawan, specializes in an AI QA Engineer platform that automates functional testing of websites using natural language. The company's vision-first, multi-modal agent technology simulates thousands of user interactions in minutes, aiming to catch bugs before they impact customers and eliminate the need for extensive manual testing.
The company highlighted its success in preventing a costly error, stating in the tweet, "catching an airline pricing bug. Spur’s AI agents did it all this year!" This achievement is consistent with previous reports indicating Spur's agents saved over $300,000 from a broken pricing experiment. This capability is crucial for consumer-facing businesses where even minor errors can lead to substantial revenue loss.
Earlier this year, on April 17, 2025, Spur secured $4.5 million in a seed funding round led by First Round Capital, with participation from Pear VC, Neo, and Conviction, among others. The funding was earmarked to further develop its AI QA Engineer and expand its team. Founders Sivakumar and Nijhawan, with backgrounds at Figma and DeepMind respectively, developed the autonomous software tester to address the pain points of traditional QA processes.
Spur’s technology is designed to put "QA on complete autopilot," enabling faster release cycles and ensuring bug-free product launches for its clientele. The company serves a growing list of enterprise customers, including e-commerce sites like LivingSpaces.com and travel platforms such as Norse Atlantic Airways and Wander.com, all of whom rely on Spur to enhance their quality analysis and prevent costly errors.