Generative AI has a firm grip on public technology discourse as of late. A brand new startup called Ema of San Francisco believes that is far more than simply a passing fancy. Today, the corporate is emerging from obscurity with an eponymous product that expects to open a brand new chapter in how AI, particularly generative AI, will change the way in which we work.
“Our goal is to construct a universal AI workforce,” said Surojit Chatterjee, CEO and co-founder, in an interview. “Our goal is to automate the mundane tasks that employees in every company perform every single day… giving them more time for more beneficial and strategic tasks.”
The company and its investors are putting money and revenue behind their words: It has already raised $25 million from a powerful list of backers and customers that it quietly amassed while still within the shadows to refute all allegations about vaporware, including Envoy, Global, TrueLayer and Moneyview.
What Ema can do is allow these corporations to make use of it in applications starting from customer support – including providing technical support to users in addition to tracking and other functions – to internal productivity applications for workers. Ema's two products – Generative Workflow Engine (GWE) and EmaFusion – are designed to “emulate human reactions” but evolve with feedback as use increases.
As Chatterjee describes it, it's not only robotic process automation (that's so 2010s), and it's not only AI to hurry up certain tasks (that goes back even further), and it's not only one other GenAI accuracy error waiting to occur mocked on social media.
Chatterjee says Ema – an acronym for “Enterprise Machine Assistant” – draws on greater than 30 large language models, he said, and “combines” them with its own “smaller, domain-specific models” in a patent-pending platform. all the issues you’ve seen with accuracy, hallucination, privacy, etc.”
This early round adds quite a lot of names to Ema's cap table. Accel, Section 32 and Prosus Ventures are co-leaders, with Wipro Ventures, Venture Highway, AME Cloud Ventures, Frontier Ventures, Maum Group and Firebolt Ventures also participating. There are also some notable individual supporters: including Sheryl Sandberg, Dustin Moskovitz, Jerry Yang, Divesh Makan and David Baszucki.
Currently, there are already dozens, perhaps a whole bunch, of corporations developing GenAI tools for enterprises, each those working on solutions for specific industries or use cases and bold, self-led style shifts like Ema's. If you're wondering why this particular GenAI startup is attracting these investors' attention, it could be partly because they're already doing business. But it also is determined by the background of the team.
Prior to Ema, Chatterjee was Chief Product Officer of Coinbase in its run-up to its IPO. Previously, he was VP of Product at Google for each the mobile ads and shopping businesses. He has around 40 patents in areas similar to enterprise machine learning software and adtech.
The other co-founder, Souvik Sen, Ema's technical director, has equally impressive experience. Most recently, he was VP of Engineering at Okta, where he led data, machine learning and devices. Previously, he worked at Google as an engineering lead for data and machine learning, specializing in privacy and security. He himself owns 37 patents.
The combined experience of those two adds weight to the corporate's ambitions and the likelihood of achieving them. But many details that might definitely play a task in development are also omitted.
For example, consider Chatterjee's expertise in e-commerce and adtech. Given that these are cornerstones of the way in which so many corporations interact with customers today, it seems inevitable that they may play a task in how Ema might evolve if successful.
On the opposite hand, if there’s a founder who has previously needed to worry about data protection and privacy, the startup can have a greater likelihood of not screwing up these issues. At least we are able to hope! It is AI, in any case, and it’s a Silicon Valley startup that can ultimately give attention to the business at hand and the usage of technology to attain it.
What's notable in the meanwhile is that ambitious startups are working to construct products that cut across different LLM silos to attain more advanced outcomes. This is probably an early sign that the LLMs are more interchangeable than you’d assume over time, and likewise more standardized.
And the flexibility to cover different use cases gives the startup potential diversification that might help grow its overall business and utility, investors say.
“Most point-to-point GenAI solutions provide high utility for specific use cases, but are either difficult to increase across use cases and even adjoining use cases, and more importantly, large enterprises are concerned about fragmentation and access to their sensitive data so many various applications,” Ashutosh Sharma, head of investment at Prosus Ventures in India, told TechCrunch. “Ema can solve these problems and deliver high accuracy with optimal return on investment.”
