Ema Raises $77 Million to Put AI Employees to Work Across Enterprise

Enterprise AI startup Ema has raised $77 million in a Series B round as it looks to push its so-called AI Employees deeper into corporate operations, targeting workflows traditionally handled by software and human teams.

The funding round was led by Bengaluru-based Creaegis, with existing investors Accel, S32 and Prosus also increasing their investments. The latest round brings Ema’s total funding to $140 million and more than quadruples its valuation from its previous round.

Founded in 2023 by former Google and Coinbase executive Surojit Chatterjee and former Okta executive Souvik Sen, Ema develops AI agents designed to carry out multi-step business processes rather than simply answer employee questions. Its platform connects with more than 250 enterprise applications and systems, with initial deployments focused on HR, IT and finance.

Ema says its technology is already being used at Fortune 500 scale. The company says revenue has increased 50-fold over the past two years, while customers typically expand their use after an initial deployment. It counts companies including Wipro, Hitachi, ADP and PwC among its customers.

One deployment at a major global systems integrator supports more than 240,000 employees across 65 countries and automates more than 100 workflows, according to Ema. The company also says another deployment handles more than 1 million IT service-management tickets annually.

The new capital will primarily support Ema’s go-to-market expansion and further development of its enterprise AI platform. The company also plans to expand into additional markets, including Asia-Pacific, South America and parts of the Middle East.

The bigger shift Ema is betting on is that enterprises may eventually buy AI based on completed work and business outcomes rather than simply paying for another software seat. That puts agentic AI directly against the traditional enterprise software model as companies look for ways to automate entire workflows rather than individual tasks.