Manot

Post-pitch story

Chinar Movsisyan founded Manot to answer a question every AI team runs into: why does my model fail in the real world when it worked fine in testing? Manot started as a platform for finding blind spots in computer vision models, pointing out the images and situations where a model is likely to get things wrong so teams can fix it before customers notice. The company came out of UC Berkeley's SkyDeck program, with early backing from SmartGateVC and angel investors, and reported raising $900,000 by March 2023. By early 2024 it had about eight employees, according to startup data sites. As AI moved toward generative tools, so did Manot. By spring 2024, its pitch focused on "reducing blind spots in GenAI applications," helping teams collect and act on feedback about where their AI products fall short. That's a natural next step, though it puts Manot in a crowded field of AI testing and monitoring tools. We didn't find any new funding announcements after the $900,000 round, and the team-size and traction numbers we found come from estimates, not the company. Manot's website is still active, and the company appears to be running. The question now is whether a small, focused team can stand out as the AI tools market keeps getting more crowded.