Surojit Chatterjee is the founder and CEO of Ema. Previously, he guided Coinbase through a successful 2021 IPO as its Chief Product Officer and scaled Google Mobile Ads and Google Shopping into multi billion dollar businesses because the VP and Head of Product. Surojit holds 40 US patents and has an MBA from MIT, MS in Computer Science from SUNY at Buffalo, and B. Tech from IIT Kharagpur.
Ema is a universal AI worker, seamlessly integrated into your organization’s existing IT infrastructure. She’s designed to boost productivity, streamline processes, and empower your teams.
Are you able to elaborate on the vision behind Ema and what inspired you to create a universal AI worker?
The goal for Ema is evident and daring: “transform enterprises by constructing a universal AI worker.” This vision stems from our belief that AI can augment human capabilities somewhat than replace staff entirely. Our Universal AI Worker is designed to automate mundane, repetitive tasks, freeing up human employees to concentrate on more strategic and helpful work. We do that through Ema’s progressive agentic AI system, which may perform a big selection of complex tasks with a set of AI agents (called Ema’s Personas), improving efficiency, and boosting productivity across countless organizations.
Each you and your co-founder have impressive backgrounds at leading tech firms. How has your past experience influenced the event and strategy of Ema?
During the last 20 years, I’ve worked at iconic firms like Google, Coinbase, Oracle and Flipkart. And at every place, I wondered “Why can we hire the neatest people and provides them jobs which might be so mundane?.” That is why we’re constructing Ema.
Prior to co-founding Ema, I used to be the chief product officer of Coinbase and Flipkart and the worldwide head of product for mobile ads at Google. These experiences deepened my technical knowledge across engineering, machine learning, and adtech. These roles allowed me to discover inefficiencies within the ways we work and find out how to solve complex business problems.
Ema’s co-founder and head of engineering, Souvik Sen, was previously the VP of engineering at Okta where he oversaw data, machine learning, and devices. Before that, he was at Google, where he was engineering lead for data and machine learning where he built one in all the world’s largest ML systems, focused on privacy and safety – Google’s Trust Graph. His expertise, particularly, is a driving force to why Ema’s Agentic AI system is very accurate and built to be enterprise ready by way of security and privacy.
My cofounder Souvik and I assumed what in the event you had a Michelin Star Chef in-house who could cook anything you asked for. You may be within the mood for French today, Italian tomorrow and Indian the day after. But no matter your mood or the cuisine you desire, that chef can recreate the dish of your dreams. That’s what Ema can do. It could actually tackle the role of whatever you wish within the enterprise with just an easy conversation.
Ema uses over 100 large language models and its own smaller models. How do you ensure seamless integration and optimal performance from these varied sources?
LLM’s, while powerful, fall short in enterprise settings as a result of their lack of specialised knowledge and context-specific training. These models are built on general data, leaving them ill-equipped to handle the nuanced, proprietary information that drives business operations. This limitation can result in inaccurate outputs, potential data security risks, and an inability to offer domain-specific insights crucial for informed decision-making. Agentic AI systems like Ema address these shortcomings by offering a more tailored and dynamic approach. Unlike static LLMs, our agentic AI systems can:
- Adapt to enterprise-specific data and workflows
- Leverage multiple LLMs based on accuracy, cost, and performance requirements
- Maintain data privacy and security by operating inside company infrastructure
- Provide explainable and verifiable outputs, crucial for business accountability
- Repeatedly update and learn from real-time enterprise data
- Execute complex, multi-step tasks autonomously
We ensure seamless integration from these varied sources through the use of Ema’s proprietary 2T+ parameter mixture of experts model: EmaFusionTM. EmaFusionTM combines 100+ public LLMs and plenty of domain specific custom models to maximise accuracy at the bottom possible cost for wide range of tasks within the enterprise, maximizing the return on investment. Plus, with this novel approach, Ema is future-proof; we’re consistently adding recent models to stop overreliance on one technology stack, taking this risk away from our enterprise customers.
Are you able to explain how the Generative Workflow Engine works and what benefits it offers over traditional workflow automation tools?
We’ve developed tens of template Personas (or AI employees for specific roles). The personas may be configured and deployed quickly by business users – no coding knowledge required. At its core, Ema’s Personas are collections of proprietary AI agents that collaborate to perform complex workflows.
Our patent-pending Generative Workflow Engine™, a small transformer model, generates workflows and orchestration code, choosing the suitable agents and design patterns. Ema leverages well-known agentic design patterns, akin to reflection, planning, tool use, multi-agent collaboration, language agent tree search (LATS), structured output and multi-agent collaboration, and introduces many progressive patterns of its own. With over 200 pre-built connectors, Ema seamlessly integrates with internal data sources and may take actions across tools to perform effectively in various enterprise roles.
Ema is utilized in various domains from customer support to legal to insurance. Which industries do you see the very best potential for growth with Ema, and why?
We see potential across industries and functions as most enterprises have lower than 30% automation in processes and use greater than 200 software applications resulting in data and motion silos. McKinsey & Co. estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually in productivity gains (source).
These issues are exacerbated in regulated industries like healthcare, financial services, insurance where many of the last many years technical automations haven’t happened because the technology was not advanced enough for his or her processes. That is where we see the largest opportunity for transformation and are seeing a number of demand from customers in these industries to leverage Generative AI and technology like never before.
How does Ema address data protection and security concerns, especially when integrating multiple models and handling sensitive enterprise data?
A pressing concern for any company using agentic AI is the potential for AI agents to go rogue or leak private data. Ema is built with trust at its core, compliant with leading international standards akin to SOC 2, ISO 27001, HIPAA, GDPR, NIST AI RMF, NIST CSF, NIST 800-171 To make sure enterprise data stays private, secure, and compliant, Ema has implemented the next security measures:
- Automatic redaction and protected de-identification of sensitive data, audit logs
- Real-time monitoring
- Encryption of all data at rest and in transit
- Explainability across all output results
To go the additional mile, Ema also checks for any copyright violations for document generation use cases, reducing customers’ probability of IP liabilities. Ema also never trains models on one customer’s data to profit other customers.
Ema also offers flexible deployment options including on-premises deployment capabilities for multiple cloud systems, enabling enterprises to maintain their data inside their very own trusted environments.
How easy is it for a brand new company to start with Ema, and what does the everyday onboarding process appear to be?
Ema is incredibly intuitive, so getting teams began on the platform is sort of easy. Business users can arrange Ema’s Persona(s) using pre-built templates in only minutes. They will tremendous tune Persona behavior with conversational instructions, use pre-built connectors to integrate with their apps and data sources, and optionally plug in any private custom models trained on their very own data. Once arrange, experts from the enterprise can train their Ema persona with just a couple of hours of feedback. Ema has been hired for multiple roles by enterprises akin to Envoy Global, TrueLayer, Moneyview, and in each of those roles Ema is already acting at or above human performance.
Ema has attracted significant investment from high-profile backers. What do you suspect has been the important thing to gaining such strong investor confidence?
We consider investors can see how Ema’s platform enables enterprises to make use of Agentic AI effectively, streamlining operations for substantial cost reductions and unlocking recent potential revenue streams. Moreover, Ema’s management team are experts in AI and have the required technical knowledge and skill sets. We even have a powerful track record of enterprise-grade delivery, reliability, and compliance. Lastly, Ema’s products are differentiated from the rest available on the market, it’s pioneering the newest technical advancements in Agentic AI, making us the go-to selection for any enterprise wanting so as to add next-generation AI to their operations.
How do you see the role of AI within the workplace evolving over the subsequent decade, and what role will Ema play in that transformation?
Ema’s mission is to remodel enterprises and help every worker work faster with the assistance of simple-to-activate and accurate agents. Our universal AI worker has the potential to assist enterprises execute tasks across customer support, worker support, sales enablement, compliance, revenue operations, and more. We’d like to remodel the workplace by allowing teams to concentrate on probably the most strategic and highest-value projects as an alternative of mundane, administrative tasks. As a pioneer of agentic AI, Ema is leading a brand new era of collaboration between human and AI employees, where innovation flourishes, and productivity skyrockets.
