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Ema, a 'Universal AI employee', emerges from stealth with $25M | TechCrunch

Mar 05, 2024 - techcrunch.com
San Francisco-based startup Ema has emerged from stealth with a product that aims to revolutionize the way we work using generative AI. The company, which has already raised $25 million from backers including Accel, Section 32, and Prosus Ventures, aims to automate mundane tasks in enterprises, freeing employees to do more valuable work. Ema's two products, Generative Workflow Engine (GWE) and EmaFusion, are designed to emulate human responses and evolve with usage and feedback. The company has already secured customers including Envoy Global, TrueLayer, and Moneview.

Ema, an acronym for "enterprise machine assistant," taps into over 30 Large Language Models and combines them with its own smaller, domain-specific models in a patent-pending platform. The startup's founders, Surojit Chatterjee and Souvik Sen, bring significant experience from their previous roles at Coinbase, Google, and Okta. The company's ability to cut across different use cases gives it potential diversification that could help grow its business and usefulness overall, according to investors.

Key takeaways:

  • A San Francisco-based startup, Ema, has emerged from stealth with a product that aims to revolutionize the way we work with generative AI. The company's goal is to create a universal AI employee that can automate mundane tasks, freeing up human employees for more strategic work.
  • Ema has already raised $25 million from a list of impressive backers and has customers such as Envoy Global, TrueLayer, and Moneview. Its products, Generative Workflow Engine (GWE) and EmaFusion, are designed to emulate human responses and evolve with usage and feedback.
  • The startup's co-founders have impressive backgrounds. CEO Surojit Chatterjee was previously the chief product officer of Coinbase and VP of Product at Google. Co-founder Souvik Sen, Ema’s head of engineering, was previously VP of engineering at Okta and an engineering lead at Google.
  • Ema's platform taps into more than 30 Large Language Models and combines them with its own smaller, domain-specific models. This approach aims to address issues with accuracy, hallucination, and data protection. The company's ability to cut across different use cases gives it potential for diversification and growth.
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