Ikigai provides a no-code platform built on proprietary large graphical models to enable prediction, sparse data reconciliation and optimization on enterprise tabular data. Customers can train models on-demand to support forecasting, scenario planning and analysis across hundreds of data sources. The founders, Devavrat Shah and Vinayak Ramesh, developed the approach while at MIT; Shah previously founded Celect (acquired by Nike in 2019) and Ramesh founded Well Frame (acquired by Blackstone in 2012). Ikigai positions its graphical models as a more storage- and cost-efficient way to apply generative AI to tabular, time-stamped, sparse enterprise data versus LLMs. The company names C3.ai, Anaplan, Dataiku and Hugging Face as competitors. With the new capital, Ikigai plans to expand its team from 30 to 70 by the end of the year and continue product development for enterprise workflows. Ikigai offers a tool to create drag-and-drop workflows that embed human decision steps and surface results in a dashboard/spreadsheet view, which the company calls an "AI-charged" spreadsheet. The product targets manual, judgment-based processes that traditional RPA cannot easily automate by letting teams stitch together disparate data sources with decision loops. The founders built the product last year based on research out of MIT, where CTO and co-founder Devavrat Shah is a computer science professor and CEO Vinayak Ramesh was a student. The company emphasizes differentiation from tools like Power BI and Airtable by focusing on decision- and data-based workflows rather than general human-involved tools. Ikigai currently has 20 employees, mostly engineers, and plans to double its headcount next year. The team includes founders with prior exits: Ramesh previously co-founded Wellframe (acquired this month) and Shah started Celect (acquired by Nike in 2019).

Total raised

$38M

Funding rounds

2

Latest round

Series A

Latest activity

Aug 2023