Organizations are awash in data, but struggle with a host of challenges to actually use, organize and analyze that data. According to one estimate, companies will store 100 zettabytes of data in the cloud by 2025. But as of now, just 13% of organizations are delivering on their analytics and data strategy, a MIT Technology Review Insights and Databricks survey found.

Devavrat Shah argues that enabling companies to effectively forecast and conduct scenario-based planning requires harnessing deep, complex data types from hundreds of sources across the business — and that AI holds the key to this. He’s the founder of Celect, an AI app for allocating and fulfilling big box retail orders (which was acquired by Nike in 2019), and directs MIT’s statistics and data science center.

“Today, the best moonshot project for AI is to bring it to organizations or enterprises,” Shah told TechCrunch via email. “Enterprises are run by experts, and we believe that these experts need to be able to work seamlessly with AI to harness the possible benefits it holds.”

To realize the mission of “empowering every enterprise with AI,” as Shah puts it, Shah started Ikigai Labs, which offers a no-code platform built on top of proprietary graphical models for prediction, sparse data reconciliation and optimization. Ikigai today announced that it raised $25 million in Series A funding led by Premji Invest with participation from Foundation Capital and E& Capital VC, bringing its total raised to $38.2 million.

Shah co-founded Ikigai alongside Vinayak Ramesh, who previously founded Well Frame, a healthcare company that Blackstone purchased in 2012. While in graduate school at MIT, Ramesh worked with Shah on building AI for tabular data — data that’s organized in a table with row and columns, like a database — using large graphical models.

Technically within the family of neural networks, graphical models represent the probabilistics relationships among a set of variables, Shah explained. “Most of the enterprise data is tabular, sparse and typically time-stamped. Large graphical models are precisely well-suited for this setting,” he added. “In modern parlance, they’re ‘generative AI’ for tabular data.”

Ikigai’s platform is designed to enable companies to create and deploy these graphical models to power apps within their organizations. Using it, customers can train models on-demand on their enterprise data, creating models that assist in forecasting, scenario planning and analysis.

One might question why Ikigai’s graphical models are superior to, say, the large language models (LLMs) that’ve gained currency in recent years. Shah notes that LLMs work well for text and other unstructured data, but also that they’re expensive to operate, requiring vast amounts of storage compared to their graphical model equivalents.

“We provide building blocks that allow customers to solve a broad spectrum of use cases,” Shah said. “We hope to bring along everyone to ride the wave of AI and not drown in it.”

Shah isn’t naive enough to think that Ikigai is without competition in the sprawling market for enterprise AI. He named C3.ai, Anaplan, Dataiku and Hugging Face as top rivals — at least in the sense that they offer at least a subset of what Ikigai offers.

But for what it’s worth, Sandesh Patnam, a managing partner at Premji Invest, is confident in Ikigai’s ability to stand out from the crowd.

“Ikigai’s founding team possesses a depth of industry and go-to-market expertise that can push the AI frontier into the center of business operations and decisions,” he said via email. “Their innovation with large graphical models will be embraced by all enterprises that look to apply generative AI to their existing tabular data.”

With the new capital, San Francisco-based Ikigai plans to grow its team from 30 people to 70 by the end of the year.