“Why do we hire the smartest people and give them jobs that are so mundane?” This was a constant nagging question during my tenure leading large product teams at Google, Flipkart and Coinbase. At Flipkart, India’s largest eCommerce marketplace, numerous data scientists were dedicated to preparing weekly reports analyzing metric movements, while thousands of customer service personnel handled refund and payment inquiries. At Coinbase, hundreds of compliance staff evaluated new user applications for account approval.

After conversations with over a hundred of enterprise leaders, it became evident that this trend was pervasive across industries. For instance, at a leading networking equipment reseller, hundreds of employees are tasked with enabling sales teams to address detailed inquiries about product specifications and cross-product compatibility. Similarly, at one of the top pharmaceutical benefits management companies, thousands of hours of pharmacists’ time is spent daily on manually reviewing patient records to determine prescription eligibility.

Amidst all the hype surrounding Generation AI (Gen AI) lately, you’d think we’d have this sorted out by now. Every CXO from every mid-sized to large enterprise is pumped about the prospect of whipping up Gen AI applications to supercharge their operations. But let’s be real here — building enterprise-grade Gen AI apps isn’t exactly a walk in the park. It resembles piecing together a complex jigsaw puzzle blindfolded, with components scattered among various vendors, and finding the right expertise feels like searching for a needle in a haystack.

So, it’s no surprise that companies are hitting roadblocks left, right, and center with their AI projects. They’re just so complicated and take ages to get off the ground. Plus, there’s the whole nightmare of data leaks. Some of these Gen AI tools out there don’t have a clue about how to keep sensitive information from slipping into those massive public language models. And trust me, you don’t want to deal with the fallout from that mess.

Oh, and here’s another fun fact: those public language models — they often tend to go off on wild tangents because they’ve never laid eyes on your top-secret enterprise data. So, if you’re aiming to build a Gen AI app that actually gets the job done, be prepared to sink thousands of hours and sky-high computational costs into it.

As one savvy CTO we spoke to put it, “Sure, you can whip up a Gen AI demo in a few hours, but turning it into a real-deal application that actually delivers ROI? That’s a whole other ball game.”

We started asking ourselves what if we could make Gen AI simple, accurate and trusted? What if we could build a true AI Employee who could collaborate with and learn from its human colleagues, take on any role in the organization and automate most of the mundane tasks?

Today, after a year of focused development, I’m excited to share that our company, Ema (short for Enterprise Machine Assistant) is emerging from stealth mode. We share with you a transformative vision, a passionate team, and a proven track record of creating impact for our customers. We believe Ema will bring in a generational shift to how enterprises work in the future. We’re delighted to see the impact Ema is already having with our current customers.

At its core, Ema functions as a Conversational Operating System, enabling enterprises to focus on business logic rather than grappling with the complexities of Gen AI implementation. With Ema, users can activate specialized AI Employees for specific roles, seamlessly collaborating with human counterparts to automate workflows efficiently.

Ema’s AI Employees operate on our patent-pending Generative Workflow Engine™ (GWE), which goes beyond simple language prediction to dynamically map out workflows with a simple conversation. Our platform offers Standard AI Employees for common enterprise roles such as Customer Service Specialists (CX), Employee Assistant (EX), Data Analyst, Sales Assistant etc. and allows for the rapid creation of Specialized AI Employees tailored to rapidly automate unique workflows. No more waiting for months to build Gen AI apps that work!

To address accuracy issues and computational costs inherent in current Gen AI applications, Ema leverages our proprietary “fusion of experts” model, EmaFusion™, that exceeds 2 Trillion parameters. EmaFusion™ intelligently combines many large language models (over 30 today and that number keeps growing), such as Claude, Gemini, Mistral, Llama2, GPT4, GPT3.5, and Ema’s own custom models. Furthermore, EmaFusion™ supports the integration of customer-developed private models, maximizing accuracy at the most optimal cost for every task.

As we unveil Ema to the world, we envision a new era of collaboration between human and AI employees, where innovation flourishes, and every employee feels empowered to unleash their creative potential. Join us as we embark on this transformative journey with Ema.