Building a New Age AI Services Company: Cognida CEO Feroze Mohammed (Part 3)

Sramana Mitra: So, I’ve a number of questions. First and foremost, you said you acquired or acqui-hired the software from somebody that you started with. What is the financing of this company? Did you raise money? Did you self-finance? How did you acquire?

Feroze Mohammed: The acqui-hire was only for one piece of software, and then we built three other foundational platforms after that. What we acqui-hired was something which had an ability to do auto ML plus kind of a platform. That’s what we built further on and called it Zuno.predict. Then we built two other platforms – Zuno.lens, which handles all the images and unstructured data using computer vision and NLP and then a third platform called Zuno.assist, which is an advanced graph rag solution. So, most of our solutions are built on top of these three foundational blocks.

Coming back to your question in terms of the initial seed, we did raise a healthy seed round. We raised about $4 million as a seed round from mostly friends, family, and people who believed in us. As a founding team, we obviously put in our money as well. But it was a pretty healthy seed round at $4 million.

Sramana Mitra: So at that point, the seed round was pitched as a product company, not as a services company.

Feroze Mohammed: At that point in time, yes, it was pitched as essentially a product plus services play, but predominantly based on the IP. The draw was around the depth of the IP that we had.

Sramana Mitra: I think in the time frame that you’re describing, 2021-2022, a services company pitch would not get funded, but it is starting to change. I think people are realizing that the game is changing. “Do it for me” services on top of AI IP is going to be a viable, high-margin business to build. And I think that is where there’s going to be a lot of interesting companies that are going to be built as services companies.

Feroze Mohammed: Absolutely, I couldn’t agree more. I think what we realized over this three-year journey as we have worked with clients—we have about 40 plus clients now in the enterprise space—is that every time a big phase shift happens, there’s an opportunity for several new age companies. We saw that during the internet era, before that in cloud, and before that in Y2K. When the advent of the internet happened, it gave rise to companies like Sapient. While there were a lot of incumbent players, it kind of gave space for new age companies. We see a similar opportunity now with this phase shift on AI.

Three things that we see: one, a huge opportunity or demand from enterprises is coming up. As AI becomes mainstream, every enterprise wants to use AI as a differentiation. However, most of the enterprises don’t have the capability within the enterprise. This requires various sets of capabilities that are difficult for enterprises to build quickly. Eventually, they would, but at this point in time, that’s a gap. For the incumbent players, it takes time to pivot and go after these opportunities. The ticket sizes of these opportunities are much smaller.

The reality is that there is a huge agency problem with the incumbents. The client account managers working for the accounts are measured with very different KPIs. They would rather go after the $10M or $20M deals in enterprise than go after, let’s say, a $200 K deal. These deals require a different kind of delivery construct. You need a fractional computer vision person, a fractional data scientist, a fractional NLP expert. You have to bring in a lot of expertise just to solve one specific problem. It’s difficult for incumbents to bring that level of capability. The talent required and the team structure needed is very different.

All three parts that we see are very different. The delivery construct is very different in this case. The commercial construct is also very different. How do you price this? Most large players are used to traditional linear pricing based on the labor spent.

Occasionally, they have some sort of outcome-based pricing. The pricing and delivery construct is very different. The supply construct is also very different. You need composite talent for solving this. You need people who understand AI, data science, Python, or whatever language they use. People also need to understand frontier models and LLM models. It’s very difficult to have such composite skills in individuals. The talent pool is very limited. So the supply construct is also very different.

All these three things being very different, it almost gives an opportunity to create a new set of companies or a new category of service players. Therein lies the opportunity before the incumbents figure this game out. All of them will eventually figure this game out and be able to play it in about twelve to eighteen months’ time. This window of twelve to eighteen months is where I think there’s an opportunity to build the next Accenture for AI, if you will, or the next talent area.

This segment is part 3 in the series : Building a New Age AI Services Company: Cognida CEO Feroze Mohammed
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