The Fractal Analytics IPO

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

But before we start, if you are someone who likes to keep track of what is happening in the world of business and finance, then press subscribe if you haven’t already. If you’re already a subscriber or you’re reading this on the app, you can just go ahead and read the story.

The Story

For years, India’s IT boom story followed a similar script – writing lines of code, building and managing systems for clients sitting abroad, all quietly in the shadows.

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

Now the divide seems to be blurring. This week, Fractal Analytics, a company behind the systems that make those decisions, is hitting the market with a IPO of ₹2,833 crore.

When ChatGPT was launched, everyone was amazed and equally worried enough to start their own AI model as soon as possible. This meant not only your new age startups, but also legacy tech firms. That’s why we’ve seen chatbots, image generation, and viral demos keep 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 that makes money today rarely looks like it. Instead, it sits within enterprises and large businesses. And unlike most AI companies that started in late 2022, Fractal Analytics started way back in the year 2000 in Mumbai. But it didn’t immediately start as an AI firm.

It has helped businesses use analytics to guide choices about marketing, supply chains and finance. You see, Fractal offered services in the data analytics and statistical modeling industry, which was still very nascent and rare at the time. Not many firms had the bandwidth to offer this to consistently high-paying clients as they do today.

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

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

Large organizations operating in many businesses and countries do not 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 should get 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 in 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 host of large enterprises. Now this is the product side of what it offers. Looking at it as a service, Fractal helps customers create generative AI systems that fit their specific business needs.

These are not short projects but long term agreements with clients in FMCG (Fast Moving Consumer Goods), Healthcare, Banking and more. The custom AI systems are deeply rooted in their clients’ internal operations.

Along with these services that Fractal provides to its customers, it also works on its own AI products through something they ‘Cogentiq‘. Instead of starting from scratch, customers can use pre-built agents, workflows and connectors to integrate AI into existing systems. In fact, 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 as 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 are not experimenting with AI. They are businesses with billions of dollars in revenue, millions of customers. They don’t buy or use AI systems the same way you and I would by downloading an app.

Once Fractal’s systems are in place, they tend to spread. A project that starts 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 customers, and those customers typically spend more over time. In practice, this creates long relationships and high switching costs.

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

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

Since this is a business-to-business AI business, customer churn tends to be lower. That’s why Fractal’s existing customers are spending more every year—a sign that once AI systems are embedded, they become part of an organization’s operating system.

And the proof is in the pudding. See how Fractal’s revenue has grown. They went from ₹2,241 crores to ₹2,816 crores from FY24 to FY25, and even turned profitable from a net loss of -₹54 crores to ₹220 crores over the same period.

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

There is one more number that clearly explains this behavior – the Net Promoter Score (NPS). It is the measure of how satisfied a customer is with the service they receive. Fractal’s NPS is consistently in the high 70s year after year. This matters because in enterprise AI, satisfaction is not about a better interface. This 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 portion of operating costs. Of the ₹1,594 crores earned till September 2025, ₹1,125 crores went into employee benefits expenditure.

Fractal’s strengths are also the source of its most significant risks. More than half of its revenue comes from its top ten customers, making the company sensitive to spending decisions by a relatively small group of businesses.

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

Fractal’s ₹2,833 crore IPO, priced at ₹857 to ₹900, is a mix of a new 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 customers. More than 25% of the new issue goes into debt repayment of Fractal USA, its American subsidiary. This move is significant because all of Fractal’s subsidiaries, the US office alone brings in 77% of the revenue.

But debt repayment is not the biggest use of funds. ₹355 crores has been reserved for research and development (R&D) in both Fractal.ai and Fractal Alpha, its two growth engines. Recognizing the importance of expansion, they have also earmarked ₹121 crores to set up new offices in India, and ₹57 crores just for the 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 to buy, allowing existing shareholders to exit their stakes.

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

Until then….

If this story helped you understand the Fractal Analytics IPO, share it with a friend, family member or even strangers WhatsApp, LinkedIn and x.

Did you know? Almost half of Indians are unaware of term insurance and its benefits.
Are you among them?

If so, don’t wait until it’s too late.

Term insurance is one of the most affordable and smartest steps you can take for your family’s financial health. This ensures that they do not face a financial burden if something happens to you.

Ditto’s IRDAI certified advisors can guide you to the right plan. Discuss a FREE 30 minute consultation and find what coverage suits your needs.

We promise: No spam, just honest advice!