Fractal Analytics IPO: India’s Enterprise AI Unicorn Hits the Public Market IPO

Introduction

Fractal Analytics Limited, a global leader in artificial intelligence and advanced analytics, is set to go public with its highly anticipated mainboard IPO. As one of India’s most prominent AI unicorns, Fractal provides strategic decision support to Fortune 500 companies. This IPO represents a landmark moment for the Indian deep-tech ecosystem, highlighting the global scale and impact of Indian AI firms.

IPO Snapshot

• Price Band: ₹857 – ₹900 per share
• Issue Size: ₹2833.9 Cr
• Type: Mainboard IPO
• Lot Size: 16 shares
• Face Value: ₹1 per share
• Dates: Feb 9 – Feb 11, 2026

Company Deep Dive

Fractal Analytics specializes in using AI and data science to help businesses make better decisions. Their solutions span across various domains, including customer analytics, operational efficiency, and personalized marketing. With a team of thousands of data scientists and engineers, Fractal has built a suite of proprietary AI products that are used by some of the world’s largest enterprises in the consumer goods, financial services, and healthcare sectors.

Financial Performance

Fractal has demonstrated strong financial growth and a path to sustained profitability. For the most recent fiscal year, the company reported a robust revenue of ₹2,765 Cr and a PAT of ₹221 Cr. These figures underscore the high value and scalability of Fractal’s AI-driven business model on a global stage.

Objectives of the Issue

The proceeds from the Fresh Issue will be primarily utilized for:

1. Investing in research and development of next-generation AI products.
2. Expanding the global sales and delivery network.
3. Strategic acquisitions to enhance technical capabilities or market reach.
4. General corporate purposes.

Risk Factors

• Intense Competition: The AI and analytics market is highly competitive with global tech giants and specialized firms.
• Talent War: Attracting and retaining top-tier AI and data science talent is a critical challenge.
• Data Privacy and Security: Handling large volumes of sensitive client data involves significant regulatory and security risks.