Genetic AI has well and truly taken hold of the public technology debate these days. A new start is called Emma from San Francisco believes it is much more than just a fantasy. It’s emerging from stealth today, with a product of the same name that it believes will open a new chapter in how artificial intelligence, specifically genetic artificial intelligence, will change the way we work.
“Our goal is to create a universal AI employee,” Surojit Chatterjee, CEO and co-founder, said in an interview. “Our goal is to automate the mundane tasks that workers do on a daily basis in every business … to free them up to do more valuable and more strategic work.”
The company, and investors, are putting money and revenue where their mouths are: it’s already raised $25 million from an impressive list of backers, along with customers it quietly raised while still in secrecy, to fend off any vaporware accusations, including Envoy Global, TrueLayer and Moneview.
As for what Ema can do, these businesses are using it in applications ranging from customer service — including offering technical support to users as well as monitoring and other functions — to internal productivity applications for employees. Ema’s two products — the Generative Workflow Engine (GWE) and EmaFusion — are designed to “mimic human responses,” but also evolve with more use with feedback.
As Chatterjee describes it, it’s not just automating robotic processes (as it is in 2010), and it’s not just AI to speed up certain tasks (which goes even further back), and it’s not just another AI failure of the generation waiting to happen. glowing on social media.
Chatterjee says Ema – which is an acronym for “enterprise machine assistant” – uses more than 30 large language models, he said, and combines them with its own “smaller, domain-specific models” in a platform that is pending for patent “to deal with all the issues you have seen accurately, illusions, data protection and so on.”
This early round adds a lot of names to Ema’s table. Accel, Section 32 and Prosus Ventures are co-leading, while Wipro Ventures, Venture Highway, AME Cloud Ventures, Frontier Ventures, Maum Group and Firebolt Ventures are also participating. Plus, there are some big supporters: Sheryl Sandberg, Dustin Moskovitz, Jerry Yang, Divesh Makan and David Baszucki among them.
There are already dozens, perhaps hundreds, of companies building GenAI tools for businesses right now, both those working on solutions for specific company verticals or use cases, and ambitious home-style swings like Ema’s. If you’re wondering why this particular GenAI startup is getting attention from these investors, some of it may be because they’re already in business. But this is also due to some background of the team.
Prior to Ema, Chatterjee was Head of Product at Coinbase which led to its IPO. Prior to that, he was Google’s VP of Product for both the mobile advertising and shopping businesses. He has around 40 patents to his name in areas such as enterprise machine learning software and ad technology.
The other co-founder, Souvik Sen, who heads Ema’s engineering, has an equally impressive background. Most recently, he was vice president of engineering at Okta where he oversaw data, machine learning and devices. and before that he was also at Google, where he was an engineering lead for data and machine learning, where he focused on privacy and security. He also holds 37 patents.
The combined experience of these two gives weight to the company’s ambitions and the likelihood that it will be able to realize them. But it also drops a lot of details that may well determine how it plays out.
For example, consider Chatterjee’s expertise in e-commerce and adtech. Since these are the cornerstones of how so many businesses interact with customers today, it’s inevitable that they’ll figure out how Ema could evolve if it takes off.
On the other hand, having a founder who has previously had to integrate and account for data protection and privacy potentially gives the startup a better chance of not screwing it up. Or at least we can hope! It is artificial intelligence after all, and this is a Silicon Valley startup that will ultimately focus on the businesses available and how to use the technology to achieve it.
Currently, it is remarkable to see ambitious startups working to build products that cross different LLM silos to achieve more advanced outcomes. It’s perhaps an early sign of how LLMs are more interchangeable than you might assume over time, and also more commoditized.
And being able to carve out different use cases gives the startup a potential diversification that could help grow its business and utility overall, investors say.
“Most Gen AI point solutions provide high value for specific use cases, but are either difficult to scale across use cases or even adjacent use cases and more importantly, large enterprises are concerned about fragmentation and access to their sensitive data from so many different applications,” Ashutosh Sharma, India chief investment officer at Prosus Ventures, told TechCrunch. “Ema can solve these problems and deliver high accuracy with optimal return on investment.”
