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LATEST POSTS📚

If you’re new, not yet a subscriber, or just plain missed it, here are some of our recent editions.

🧠 10 Things Interesting To Me Right Now. A quick look inside to the brain of one man, me. 
🎯 Leading Intercom’s AI Transformation. Des Traynor on zero-to-one mess, AI momentum, and leading with intent.
🗝️ Unfiltered: Vanta’s Product & Growth Leaders. Vanta leaders spill the beans on how they are going from 1 to 100.

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HOUSEKEEPING 📨

I’m starting to get settled back into Melbourne life. One highlight has been setting up everything from scratch. New house, bed, desk, etc. My new bike, for example, is tricked out pretty nicely. We’ve also been going hard on quality items. Things that you only need to buy once.

I’m pretty excited about two things: my new adjustable standing desk and this laptop chest harness. Yep, it’s a chest desk. I live next to Melbourne’s best lake, Albert Park. My plan is for every day when there’s no rain, no wind, and no sun, to work while I walk laps around the park. I used to do this in the past. Work from parks, tethering off my phone. This would be a significant upgrade, and I’m pretty excited about it. I’ll let you know how I go. Anyway, enjoy the piece!

INTERVIEW 🎙️

Surojit Chatterjee, Founder & CEO at Ema

Surojit Chatterjee, the Founder & CEO at Ema, is a veteran product and engineering leader who has spent his career driving innovation at the world’s most influential technology companies. As Vice President at Google, he helped scale Mobile Ads and Google Shopping into multibillion-dollar businesses, and later joined Coinbase as Chief Product Officer, where he guided the exchange through a period of explosive growth and brought institutional rigor to one of crypto’s earliest pioneers.

Today, Surojit channels that deep operational experience into his role at Ema, an AI company reimagining workplace productivity by creating ‘AI employees’ that autonomously handle up to ninety percent of routine tasks in customer support, HR, and sales. Under his leadership, Ema’s platform has been adopted by Fortune-level enterprises seeking to free their teams from repetitive work and unlock new capacity for creative problem-solving.

What is your main day-to-day job as CEO?

As the early-stage CEO of Ema, my day-to-day job is doing whatever needs attention—literally anything and everything. Some days I’m cleaning coffee cups in the kitchen before an important visitor arrives, other days I’m hammering out a complex contract with legal, or diving deep into product brainstorming and whiteboarding with the team.

No job is too small—sometimes it’s assembling furniture or troubleshooting a system's glitch—because in a startup, every detail matters. As we grow and can delegate more, my role will shift, but I still believe the superpower of any leader is the ability to roll up their sleeves and go into the weeds whenever it’s needed.

Who are your direct reports?

My direct reports are my technical co-founder, who serves as our head of engineering and operations and strategy. She handles everything outside of engineering, including product, sales and marketing, finance, HR, you name it. So we’re a very small, lean team: I’m out selling, my co-founder is building, and she’s keeping everything together.

Tell us the problem you’re trying to solve?

I worked at some really good companies—Google, Coinbase, and a few others—for a long time. And what I saw was that even at these places, with top-tier talent, around 50% of the time was spent just keeping the lights on. We had amazing, creative people. But they were often stuck doing mundane, repetitive work. Just grinding to keep the business running, instead of focusing on things that actually added value. And if you look at non-tech companies, that number jumps to 70%, 80%, even 90%. People are doing the same repetitive stuff that doesn’t really move the needle.

Incremental new value is what keeps the business running. That’s important. But when this whole generative AI revolution kicked off, the question in my mind was: how do we use this technology to create what I call AI employees? These AI employees are kind of like humans. They can reason, they can think, and most importantly, they can take action within an enterprise. They can handle a lot of the repetitive tasks that humans currently do.

That frees people up to focus on more interesting, high-impact work. That’s really the problem we’re trying to solve: building something close to general intelligence for the enterprise. Entities that can act like humans, think like humans, and collaborate with humans.

Have you tried to quantify how much time Ema is saving?

Absolutely. Today, Ema helps Fortune 1000 enterprises, and some mid-market clients, across three major areas: customer support, HR and employee experience, and sales and marketing automation, though we also support finance, legal, and other functions. In customer support, our AI employees often eliminate eighty to ninety percent of the workload, handling even the most complex cases end-to-end with little or no human intervention and freeing skilled teams to focus on higher-value work.

In HR and employee experience, we just launched with Hitachi—covering 120,000 employees across four companies—to fully automate hire-to-retire processes and multiple use cases. Roughly seventy percent of the time previously spent by humans on routine inquiries is now saved because employees get instant answers from Ema instead of waiting on manual responses.

Who is your ICP, and how did you identify them?

Our typical ICP is chief AI officers, CTOs, CIOs. Those who are looking to transform their entire business through what we call agentic business transformation. How did we find them? From the very beginning, we focused on increasing productivity across the board, horizontally, and we typically go to the C-level to discuss kind of how we can do this agentic business automation for multiple functions.

It turns out customer support, HR, and sales are the first three areas where there’s a lot of interest, though we also have Ema users in finance, legal, healthcare, prior-authorization, operations, and a variety of other areas.

Is there any sort of implementation team or is it plug-and-play?

Great question. The way our product is structured is around what we call pre-built AI employees, which cover the canonical use cases we see over and over; like customer support or HR. You simply plug them in, connect your applications and databases through our pre-built connectors—whether it’s your file system, Slack, email, or whatever else—and they’ll be live in production in six to eight weeks at most. Honestly, a lot of that time is just spent finding the right person to approve the connection and gathering the necessary data, not on building the integration itself.

If you need something beyond a pre-built AI employee, we have a no-code, drag-and-drop platform that lets you convert any human-led business process into an agentic workflow with a fully conversational interface. That layer usually takes a few extra weeks because you’re mapping out that unique process for the customer, but even then, it’s dramatically faster than a traditional consulting engagement that could take nine months to a year. All of it can be done in a matter of weeks now, and that’s what makes this technology so exciting.

How do you think about pricing given that the value you’re creating is in time saved?

So we made a decision very early on—back in late 2022—when we started building Ema. When we presented to investors in the Valley, most of them didn’t even understand what an ‘AI employee’ was or why we weren’t just building another model. Our AI employees are multi-agent systems that come together to mimic a human role. In terms of pricing, we chose not to charge by seats—SaaS companies do that, and customers end up overpaying for software—so instead, we price based on outcomes and consumption.

Outcome naturally varies by use case: for customer support, it’s the number of tickets resolved; for sales automation, where our AI employees generate responses to RFPs or build slide decks on demand, it might be the length of each deck or the number of documents produced. In every case, our pricing aligns directly with the value delivered.

What was the most difficult thing when going from zero to one?

The toughest part was blocking out all the noise. Everyone, especially investors, telling you they’ve seen similar ideas or asking why you weren’t just building another model. You have to hold tight to your conviction that you’re creating something unique. On day one, my entire pitch deck was maybe two slides, and looking back, I’m amazed I convinced anyone—investors or early employees—buy into something so raw.

But the core idea never wavered: build a universal AI employee from the very beginning. It wasn’t polished or fully fleshed out, yet that conviction carried us. Convincing your first hires to share that belief is the same challenge. So in a way, getting started and standing by your vision is half the battle won.

What principles have you carried from Coinbase and Google?

I’d say there are three core principles I brought over. First, deep customer empathy. Really understanding what people need, not just the solution they have in mind. At Google and Coinbase, we did extensive customer research and rapid experimentation to uncover the right problems to solve.

Second, build for both newcomers and power users: the product must be incredibly easy for a first-time user to adopt, yet also expose the advanced ‘power tools’ that seasoned users need—much like Google Maps, where anyone can start with a simple search but power users can dive into layers, APIs, and custom contributions. Third, ship fast and iterate constantly.

The underlying AI technology and market demands shift so quickly that you need a relentless weekly cadence of improvements. Google moved quickly in its early days, Coinbase operated at one notch above that, and at Ema we’re moving even faster because our small, agile team can deliver updates in a matter of days.

Do you run hybrid, onsite, or remote, and why?

We run hybrid, with everyone onsite three days a week. And most people actually come in four or five days. We have two physical offices—Mountain View in Silicon Valley and Bangalore—so each location operates locally but still mandates in-office presence. Early-stage innovation thrives on whiteboard sessions, spontaneous hallway conversations, and live brainstorming—serendipitous collisions you just can’t get over video.

At the same time, you need quiet stretches to dive deep into coding or design. By clustering face-to-face collaboration into three on-site days and reserving the other days for focused work, we strike the right balance between creativity and productivity. It’s a model that’s served Ema really well.

💡 Hiring remote AI / ML engineers yourself? If so, hit us at Athyna, we can help.

How do you get the best out of yourself personally?

Great question. I think the first thing is, in terms of mindset, I always feel there’s a lot for me to learn and I’m never fully satisfied—never ‘too happy,’ which drives me toward continuous improvement. That constant sense of ‘how can we do better?’ is baked into our culture: improving the company, our processes, the people, and ourselves.

We put that into practice in hiring. We deliberately seek out people who are better than our current team—and better than me—in one or two dimensions. I’m always looking for those E-shaped folks who can do many things but truly excel in specific areas, because I can learn from them every day. And it doesn’t stop at senior leaders; I’ll go to junior team members—someone who’s done customer success for years, for example—to understand their craft and absorb their expertise.

That humility—being open to new ideas, constantly learning from everyone around you, and building a network of people who teach you—is what keeps me at my best, both personally and professionally, even when the pressure is on.

And that's it! You can connect with Surojit on LinkedIn and X. To learn more about Ema, check out the website here.

BRAIN FOOD 🧠 

TWEETS OF THE WEEK 🐣 

TOOLS WE USE 🛠️

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See the full set of tools we use inside of Athyna & Open Source CEO here.

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