In today’s Finshots, we break down the Fractal Analytics IPO, which opens for subscription today and closes on February 11th.

The Story

For years, India’s IT boom story followed a similar script ― write lines of code, build and manage systems for customers sitting abroad, all quietly in the shadows. 

The key decisions behind running the business are left somewhere else.

Now that separation looks like it’s beginning to blur. This week, Fractal Analytics, a company behind the systems that make those decisions, is headed to the market with a ₹2,833 crore IPO.

When ChatGPT launched, everyone was astonished and equally worried enough to start their own AI model as soon as possible. That didn’t just mean your new age startups but legacy tech firms as well. That’s why we saw chatbots, image generation and viral demos that kept getting better. And also why the construct of AI in our minds looks a lot like an everyday-use large language model (LLM).

But here’s the catch: the AI model today that makes money rarely looks like that. Instead, it sits inside enterprises and big businesses. And unlike most AI companies that took off in late 2022, Fractal Analytics started way back in the year 2000 in Mumbai. But it didn’t start right off the bat as an AI firm.

It was helping enterprises use analytics to guide choices across marketing, supply chains, and finance. You see, Fractal was offering services in the data analytics and statistical modelling industry, still very nascent and rare at the time. Not many firms had the bandwidth to offer it to consistent high paying customers like they do today.

Their analytics experience then led to machine learning in the 2010s and eventually into the kind of enterprise AI systems it now sells.

Today, Fractal Analytics calls itself an ‘enterprise AI’ company. If that sounds like tech jargon, we got you. Let’s look at it from the perspective of what problem they’re trying to solve.

Large organisations that operate in many businesses and countries don’t struggle without having less data. If anything, they struggle with what to do with it and how to manage it properly. Which customers are important. Which product needs attention. What risks to flag. That’s Fractal’s job ― build the systems that answer those questions at scale.

Most of this data-heavy work sits inside Fractal.ai, its primary product that accounts for nearly all of its total revenue. Through this segment, Fractal sells a mix of AI-driven services and proprietary systems to a bunch of large enterprises. Now this is the product side of what it offers. If you look at it as a service, Fractal helps clients create generative AI systems that fit their specific business needs.

These aren’t short projects, but long term agreements with customers in FMCG (Fast-moving consumer goods), healthcare, banking and more. The custom-built AI systems are deeply rooted into their customers’ internal operations.

With these services that Fractal provides to its customers, it’s also working on its own AI products through something they call ‘Cogentiq’. Instead of starting from scratch, clients can use pre-built agents, workflows, and connectors to integrate AI into existing systems. In effect, Fractal is trying to move from selling intelligence project by project to packaging parts of it as a platform.

One way to think about Cogentiq is as an enterprise AI suite. Just like Adobe packages different creative tools into a single platform, Cogentiq brings together multiple AI agents, workflows, and decision applications under one umbrella — designed to be deployed together, rather than as standalone tools. But unlike consumer software suites, Cogentiq is built for large enterprises and is rolled out alongside Fractal’s services.

The companies that buy Fractal’s services aren’t experimenting with AI. They’re businesses with billions of dollars in revenues, millions of customers. They don’t buy or use AI systems the same way that you and I would by downloading an application.

Once Fractal’s systems are in place, they tend to spread. A project that begins in one function often expands to others, across geographies and teams. That expansion is visible in the company’s numbers: a majority of its revenue comes from existing clients, and those clients typically spend more over time. In practice, this creates long relationships and high switching costs.

As of September 2025, Fractal works with over 120 of what it calls “Must Win Clients”, which bring in recurring revenue and multi-year agreements. These big ticket clients include Citibank, Nestle, Costco, Mars, Mondelez, and Philips.

More than half of its revenues come from its top 10 clients, most of whom have been around with them for over eight years. If you were to lay out their customers on a world map, most of them would be sitting in the United States (64.9%), Europe (21%) and India (7.6%).

Since this is a business-to-business enterprise AI business, client churn tends to be lower. That’s why Fractal’s existing customers spend more each year — a sign that once AI systems are embedded, they become part of an organisation’s operating fabric.

And the proof is in the pudding. Take a look at how Fractal’s revenues have grown. They went from ₹2,241 crores to ₹2,816 crores from FY24 to FY25, even turning profitable from a net loss of -₹54 crores to ₹220 crores over the same period.

One number quietly captures this dynamic. Fractal’s existing clients spend more with the company each year, pushing its net revenue retention above 110%. In simple terms, even without signing new customers, Fractal’s revenue base tends to grow.

There’s one more number that clearly explains this behaviour — the Net Promoter Score (NPS). It’s the measure of how satisfied a customer is with the service they receive. Fractal’s NPS consistently sits in the high 70s year after year. That matters because in enterprise AI, satisfaction isn’t about a better interface. It determines whether a project expands to another team, another geography, or another year.

In today’s AI boom, profitability is the exception, not the rule. For Fractal, that profitability still comes with a caveat. Like most enterprise AI firms, its biggest cost is people. Highly skilled data scientists, engineers, and domain experts don’t come cheap, and employee expenses make up a large share of operating costs. Of the ₹1,594 crores earned until September 2025, ₹1,125 crores went in employee benefits expenses.

Fractal’s strengths are also the source of its key risks. More than half of its revenues come from its top ten clients, making the company sensitive to spending decisions by a relatively small group of enterprises. 

Geographic exposure adds another layer of risk. With nearly two-thirds of revenues originating from the United States, Fractal is closely tied to North American enterprise tech budgets. Any slowdown in IT and AI spending, changes in regulations, or currency volatility could affect growth and margins sharply.

Set at a price band of ₹857 to ₹900, Fractal’s ₹2,833 crore IPO is a mix of a fresh issue and an offer for sale. ₹1024 crores raised will go into the company itself, giving it capital to invest in its AI platforms, expand into new markets, and deepen relationships with large enterprise clients. Over 25% of the fresh issue will go into debt repayment of Fractal USA, its American subsidiary. This move is significant because of all Fractal’s subsidiaries, the USA office alone brings in 77% of revenues.

But debt repayment isn’t the biggest use of funds. ₹355 crores is reserved for research and development (R&D) into both Fractal.ai and Fractal Alpha, its two growth engines. Recognising the importance of expansion, they’ve also earmarked ₹121 crores to set up new offices in India, and ₹57 crores just for purchase of laptops for their employees. The rest will be used for general corporate purposes and inorganic growth.

The remaining ₹1,810 crores is an offer for sale, letting existing shareholders pare down their stakes.

If you look at the valuation, the IPO prices the company at ₹15,400 crore, which is a whopping 79x its FY25 earnings. And since this is India’s first-ever AI company to go public, it’s hard to judge whether that price is fair. But that also makes it hard to compare or replace. Whether the market embraces it or not, is something that we’ll have to wait and see.

Until then….

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