Ikigai Secures $25M Investment to Transform Tabular Data with Cutting-Edge Generative AI Technology

Organizations today are inundated with data, yet they face significant challenges in utilizing, organizing, and analyzing it effectively. An estimated 100 zettabytes of data will be stored in the cloud by 2025. Despite this massive growth, only 13% of organizations are successfully executing their analytics and data strategies, according to a survey by MIT Technology Review Insights and Databricks.

Devavrat Shah emphasizes that effectively forecasting and conducting scenario-based planning requires leveraging complex data types from diverse sources within a company—and he believes AI is the solution. As the founder of Celect, an AI application designed for optimizing large retail orders (acquired by Nike in 2019), and the director of MIT’s Statistics and Data Science Center, Shah has a unique perspective.

"Today, the most ambitious project for AI is integrating it into organizations," Shah shared via email. "Businesses are led by experts who need to collaborate effortlessly with AI to unlock its full potential."

To fulfill his vision of "empowering every enterprise with AI," Shah co-founded Ikigai Labs, which provides a no-code platform built on proprietary graphical models for prediction, sparse data reconciliation, and optimization. Recently, Ikigai announced a successful $25 million Series A funding round led by Premji Invest, with involvement from Foundation Capital and E& Capital VC, bringing its total funding to $38.2 million.

Shah teamed up with Vinayak Ramesh to co-found Ikigai. Ramesh previously founded Wellframe, a healthcare company acquired by Blackstone in 2012. While pursuing graduate studies at MIT, Ramesh collaborated with Shah to develop AI solutions focused on tabular data—structured data organized in rows and columns.

Graphical models, a type of neural network, articulate the probabilistic relationships among various variables, Shah explained. "Most enterprise data is tabular, sparse, and typically time-stamped. Large graphical models are particularly well-suited for these conditions," he noted. "Essentially, they serve as 'generative AI' for tabular data."

Ikigai's platform empowers companies to create and deploy these graphical models to enhance their applications. Clients can train models in real-time using their enterprise data, generating insights for forecasting, scenario planning, and analysis.

You might wonder why Ikigai’s graphical models are advantageous compared to the emerging large language models (LLMs). Shah points out that while LLMs excel at handling text and unstructured data, they tend to be costly to operate and require significant storage compared to graphical models.

"We provide foundational tools that enable customers to address a wide range of use cases," Shah explained. "Our goal is to help everyone navigate the AI landscape without getting overwhelmed."

Shah is well aware that Ikigai faces stiff competition in the vast enterprise AI market. He identified C3.ai, Anaplan, Dataiku, and Hugging Face as significant competitors that offer some overlap with Ikigai’s services.

However, Sandesh Patnam, managing partner at Premji Invest, expressed strong confidence in Ikigai's potential to differentiate itself. "The founding team at Ikigai brings extensive industry expertise and go-to-market strategies that will advance AI integration into core business operations," he stated via email. "Their innovative approach with large graphical models is poised to resonate with enterprises eager to implement generative AI in their tabular data."

With its new funding, the San Francisco-based Ikigai aims to expand its team from 30 to 70 employees by year-end.