Introducing Julius AI, the brainchild startup of Y Combinator alumni Rahul Sonwalkar. Rising through the ranks as an AI data analyst, Julius AI just secured an incredible $10 million in seed funding, supported by Bessemer Venture Partners.

It's not just Bessemer Venture Partners who've shown faith in Julius AI. The startup has also received plaudits from Horizon VC, 8VC, Y Combinator, AI Grant accelerator, and many more respectable angel investors, like Jeff Lawson of Twilio, Guillermo Rauch of Vercel, and Aravind Srinivas of Perplexity, to name a few.

Sonwalkar launched Julius AI in 2022 after rising from Y Combinator, and it is a major shift from his prior logistics firm. The AI platform, designed to mimic a data scientist, analyzes extensive datasets, performs predictive modeling from natural language prompts, and generates over 10 million visualizations for its user base of over 2 million.

Just ask Julius AI a question as you would a teammate and let the AI work its magic. As Sonwalkar explains it, this AI platform runs code and delivers analysis at your command, not unlike an analyst on your team. Julius AI's ability to answer complex questions and even visualize revenue and net income correlations for different industries in China versus the US is remarkable.

Julius AI, with its specialization in data science, captured Professor Iavor Bojinov's attention at Harvard Business School. So much so, that Bojinov requested a modification of Julius AI for a new required course,Data Science and AI for Leaders.

Sonwalkar firmly stresses the value of focusing on a use case in response to doubts on Julius AI's connection to features from foundational model companies. His time at YC also saw him create a viral prank featuring a pseudo-layoff after Elon Musk's acquisition of Twitter. Despite the buzz from the prank, Sonwalkar believes Julius AI is his real claim to fame.

"People rarely connect me with the prank," he shared. “I'm now more known for Julius.” With Julius AI's promising trajectory, we'd be hard pressed not to agree.