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San Francisco-based Ema, the AI agent startup founded by former Google and Okta employees, is making waves by raising an additional $36 million in a Series A fundraising round today.
This latest investment brings the company’s total funding to $61 million and is led by Accel and Section 32.
Ema plans to utilize this funding to enhance its proprietary technology, enabling enterprises to easily configure and deploy no-code AI agents — known as “universal AI employees” — capable of handling a wide range of tasks across various functions.
“At Ema, our mission is to automate mundane tasks currently performed by human employees, freeing them up to focus on more valuable work within the enterprise. Ema serves as a universal AI employee, capable of adapting to any role within the organization — from customer support and employee experience to sales & marketing and legal & compliance,” explained Surojit Chatterjee, the CEO and co-founder of the startup, in an interview with NeuralNation.
Since emerging from stealth mode a few months ago, Ema has gained significant traction, deploying its AI employees across organizations in sectors such as fintech, legal, healthcare, and e-commerce.
While the new funding is a testament to the confidence in the company’s technology, the competitive landscape remains challenging. Several vendors have entered this space over the past year, leveraging foundational models to empower enterprises with AI agents ready for use.
What does Ema bring to the table?
Prior to the emergence of OpenAI’s ChatGPT in late 2022, enterprises relied on rigid, flow-based chatbots for automating basic tasks like customer support.
While these solutions served their purpose, they often fell short in providing the contextual knowledge and learning required to meet customer expectations.
However, with the introduction of large language models (LLMs), the capabilities of these chatbots received a significant upgrade. This evolution led to the concept of powerful AI agents — LLM-powered systems that not only offer accurate answers but also execute complex actions across multiple enterprise applications, working with diverse data types.
Imagine an AI agent for customer support that can actually cancel your order upon request, instead of just directing you to the cancellation page.
With the concept of a universal AI employee, Ema is delivering this precise experience, providing enterprises with an agentic system capable of assuming any role within the organization, from managing customer service and technical support to supporting sales and marketing efforts.
No-code agentic platform and AI employee templates
At its core, the company offers a no-code agentic platform that grants users access to a library of pre-built AI employee templates.
Once a user selects an agent for a specific use case, the platform guides them through a conversation to quickly fine-tune and deploy the finalized AI employee (or Ema persona) for decision-making, planning, and orchestrating enterprise workflows — all while seamlessly collaborating with human counterparts.
“Ema empowers enterprise customers to create customized personas by defining goals, resources, and constraints. This level of customization was previously reserved for AI experts and data scientists. Now, with just a few guided conversations and simple configuration steps, enterprises can create and deploy new AI employees tailored for specific roles within their organization, faster than ever before. This capability not only expands the reach of Agentic AI but also democratizes it,” Chatterjee emphasized.
Behind the scenes, Ema’s agent deployment process is powered by a Generative Workflow Engine, a compact transformer model that generates workflows and orchestration code, selecting suitable agents and design patterns. When configuring the agent, users can connect their desired data sources and applications with a library of over 200 connectors.
This approach ensures that the deployed AI employee possesses contextual awareness (including documents, logs, data, code, and policies) and the ability to take actions across systems.
To maintain accuracy post-deployment, the company leverages a 2T+ parameter mixture of experts model called EmaFusion. This model combines over 100 public LLMs and domain-specific custom models to maximize accuracy at a minimal cost.
Chatterjee also confirmed that users have the option to integrate private custom models, trained on their own data, to guide the behavior of their AI employee. Additionally, the company has implemented robust data protection and security measures to ensure the confidentiality and security of all enterprise data processed by the agent.
“We have implemented stringent systems for secure redaction and de-identification of sensitive data, thorough copyright violation checks (in document generation scenarios), end-to-end encryption of data in transit and at rest, comprehensive audit logging, real-time monitoring, output explainability, and regular penetration testing. We are fully compliant with the highest international standards,” he assured.
Goal to expand in a competitive market
While specific revenue and customer details were not disclosed by the CEO, he highlighted that the number of companies utilizing Ema has tripled since the company emerged from stealth mode in March 2024, spanning industries such as fintech, legal, healthcare, e-commerce, and insurance.
“Enterprises like Envoy Global, TrueLayer, and Moneyview have engaged Ema for multiple roles, and in each capacity, Ema has demonstrated performance on par with or exceeding human capabilities,” the CEO shared.
Looking ahead, the CEO outlined plans to use the funding to advance the company’s technology and expand its go-to-market team to better address the growing demand from potential clients.
As Ema navigates this competitive landscape, it will be intriguing to observe how the company continues to differentiate itself in a rapidly expanding market. Other prominent players venturing into conversational AI agents for enterprises include Decagon, Yellow AI, Cognigy, Rasa, and Kora AI.
Even Bret Taylor, a board member at OpenAI, has entered the arena with a startup named Sierra, which has secured $110 million in funding from notable venture capital firms. Sierra is racing to leverage large language models to enable enterprises to build always-available AI agents tailored to their businesses.
