Sanket Modi

Fractal Analytics has cut its IPO size by over 40% to $313.6 million, trimming the issue to ₹2,834 crore from ₹4,900 crore. The Mumbai-based firm is set to become India’s first listed pure-play AI company. The IPO includes a fresh issue of ₹1,024 crore and an offer-for-sale of ₹1,810 crore, with retail bidding opening February 9 and closing February 11. Anchor investors can bid on February 6. Co-founders Srikanth Velamakanni and Pranay Agrawal, who own about 20%, are not selling shares. Proceeds will support debt repayment, new offices, and R&D focused on generative AI. Read the Full Report: https://lnkd.in/gVPDK-hx

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Fractal Analytics, one of India’s leading pure-play artificial intelligence and analytics companies, has filed draft papers with SEBI for a ₹4,900 crore IPO. The offering will include a fresh issue of ₹1,279.3 crore and an offer for sale (OFS) worth ₹3,620.7 crore by existing shareholders, including major investors Quinag Bidco (₹1,462.6 crore) and TPG Fett Holdings (₹1,999.6 crore). About 75% of the offer is reserved for QIBs, 15% for NIIs, and 10% for retail investors, with a 5% employee reservation. Proceeds from the fresh issue will be used to invest in Fractal USA for debt repayment, set up new offices in India, bolster R&D and marketing under Fractal Alpha, and pursue acquisitions and strategic initiatives. Notably, Group CEO Srikanth Velamakanni and CEO Pranay Agrawal, who each own around 10% of the company, are not selling their stakes. Founded in 2000 by five IIM Ahmedabad graduates, Fractal reached unicorn status in 2022 and now supports large global enterprises across sectors such as CPG & retail, TMT, healthcare, life sciences, and BFSI. The company’s strong growth trajectory is evident, with FY25 revenue rising 25.9% to ₹2,765 crore from ₹2,196 crore in FY24, while profit after tax surged to ₹220.6 crore from a loss of ₹54.7 crore the previous year. Backed by marquee investors like TPG, Apax, and Gaja, Fractal continues to strengthen its position as a domain leader in AI-driven decision-making. Kotak Mahindra Capital, Morgan Stanley India Company, Axis Capital Ltd., and Goldman Sachs (India) Securities are acting as the book-running lead managers for the IPO.

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Fractal Analytics, the Mumbai and New York-based AI–driven analytics company, has filed a draft red herring prospectus (DRHP) with the Securities and Exchange Board of India (SEBI) for a Rs 4,900 crore (about $590 million) initial public offering (IPO), in what would be its second bid to go public. The offering will comprise a fresh issue of shares worth up to Rs 1,279.3 crore and an offer for sale of up to Rs 3,620.7 crore. Private equity backers Apax Partners and TPG will account for the bulk of the sale, offloading shares worth Rs 1,462.6 crore and Rs 1,999.6 crore, respectively, through their affiliates. Read the full story here:👇 https://lnkd.in/dTHGnym2 #analytics #drhp #sebi #funding #mumbai #company #startup #indianstartupnews

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15 years back, when we were building voice bots for the US market - one of the key debates used to be whether to declare or not to declare its a bot. Even if not explicitly say things like "I can help you with X,Y, Z queries and you can say things like .... " indicating a limited understanding. Even if the users did not understand what a virtual assistant is - they understood this is not a human being speaking on the other side. European regulations strictly mandate the voice agents to declare before proceeding in a conversation. India is an upcoming market but getting into the space at different stage of maturity of conversational engines - Hence there is a lot of asks to cover up the underlying bot and not by any means show up its a machine. The experience should be "human like" but that does not mean we should hide the machine behind. For an end consumer, its important to know the species they are speaking to and I now see why some of the western paradigms firmly support the declarative approach. Its just more human.

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Fractal Analytics, the AI-powered SaaS unicorn, has raised INR 48.7 crore (approximately $5.6 million) from its co-founder and CEO, Srikanth Velamakanni, as part of the conversion of his partly paid-up equity shares into fully paid-up shares. Investment Details Velamakanni’s investment of INR 48.7 crore covers the conversion of 6.6 lakh partly paid-up equity shares into fully paid-up shares at an issue price of INR 733 each. This move signals continued confidence from the company's leadership ahead of its public listing. Employee Stock Allotment In addition to Velamakanni’s investment, Fractal's board approved the allotment of 1.32 lakh equity shares to 81 employees under its Employee Stock Ownership Plan (#ESOP), aligning the interests of key personnel with the company’s future growth. Preparing for IPO Fractal is gearing up for a $500 million IPO, with plans to file its #DRHP in August. The company has appointed leading investment banks, including Morgan Stanley, Kotak Mahindra Bank, and Axis Capital Ltd., to manage the public issue. Financial Growth and Challenges Despite posting a net loss of INR 54.7 crore in FY24, Fractal’s revenue grew by 11%, reaching INR 2,196.3 crore. The company is investing in R&D to fuel its growth and product innovation in the AI and analytics space. Fractal’s upcoming IPO is set to be one of the most anticipated in the SaaS sector, providing a significant growth opportunity for investors in the booming AI market. Team Fractal✅ Srikanth Velamakanni | Pranay Agrawal | Ashwath Bhat | Anish Padinjaroote | Veena Bramhakal | Ajoy Singh | Himanshu Nautiyal | Rohini Singh | Sagar Shah | Reshma Takariya | ManMohan Arora | #unicornStartups #saasStartups #aiTechstartups #UpcomingIPO #startupUpdates #startupro

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Fractal Analytics IPO: India's 1st AI Unicorn? | ₹4,900 Cr Issue Analysis | DRHP Breakdown Fractal Analytics, India's first AI unicorn, has filed its DRHP for a massive ₹4,900 Crore IPO! This video is a complete breakdown of everything you need to know. 👉 For more IPO updates Visit: https://lnkd.in/ezSPymWW #IPOreview #FractalIPO #FractalAnalytics #Investing #StockMarket #AI #ArtificialIntelligence #Unicorn #DRHP #FractalIPO #FractalAnalytics #FractalAnalyticsIPO #FractalDRHP #IPO #IPOInvestment #IPOReview #AI #ArtificialIntelligence #Unicorn #TechIPO #IndianTech #Analytics #UpcomingIPO #DRHP #PrimaryMarket #KotakSecurities #KotakNeo

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It is hard to understand Fabric's long-term strategy. On one hand, it was smart to build it around PBI, the leading BI platform. On the other hand, the key to Power BI's success in dethroning Tableau was its' extremely affordable licensing. Competitive licensing is not an element that Fabric's always-on pricing model can boast of at current rates, especially when the likes of Databricks, Snowflake, and other rising platforms are around. Perhaps Microsoft will be content with Fabric always being the 3rd/4th best option?

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Founded in 2013 by Shub Bhowmick, along with Sumit Mehra and Shashank Dubey, Tredence Inc. was built to bridge a persistent gap in enterprise AI where insights look good on dashboards but fail to drive real business outcomes. What began as a mission to operationalise analytics has grown into a 4,000-strong data science and AI company serving 100+ global clients, including Fortune 500 firms like PepsiCo, Mars, and Unilever. Headquartered in the US, with delivery and R&D centres across Bengaluru, Chennai, Pune, and Kolkata, Tredence focuses on decision intelligence, generative AI, and agentic AI systems tailored to real-world use cases. With revenue growing at ~40% YoY and a clear path to $1B in annual revenue, Tredence is betting big on responsible AI, deep domain expertise, and measurable impact. Read more: https://lnkd.in/gzc6xRSS By Tunir Biswas #StartupInfocus #Tredence #AI

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Accuracy doesn’t come from bigger models — it comes from grounding. Microsoft’s Fabric latest release weaves Graph + Maps + MCP into the enterprise stack For CDAOs and data leaders, here’s what matters: 1. Graph + Maps come to Fabric Adding it native to fabric and simplifying use is a reinforcement that context is the new control plane with the rise of AI. 2. Official MCP Server The Model Context Protocol makes the Fabric agent-ready. No more custom plumbing. Fabric assets (tables, pipelines, models) can be more safely exposed to AI agents, right inside and leveraging your Microsoft governance framework. Bonus: It’s already integrated into VS Code and GitHub Codespaces, so developers can build within their daily workflow. 3. LinkedIn Graph under the hood This is the quiet but strategic play. Microsoft is operationalizing LinkedIn’s proven graph tech in Fabric. Today, it grounds agents. Tomorrow, it could bridge LinkedIn’s people/skills/company graph into enterprise solutions and decisioning. 💡 Why it matters: Fabric is shaping up as Microsoft’s answer to the data-to-decision stack: not just unifying data, but grounding AI agents in enterprise context, governance, and graph-rich reasoning. Rivals have MCP servers too, but Microsoft, as it has done in the past, is bundling. Bundling governance, graph, and developer experience into one package. Check out the Microsoft release. I put a link below👇🏻 Question for you: Would grounding your AI agents in a graph and geospatial context change your 2026 roadmap? #Fabric #Microsoft #CDAO #DecisionAutomation #GraphRAG

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The dashboard said 94% success. The humans said they were miserable. Who was right? Watch the short clip 👇 This is what Praveen Tomar (Head of Data Automation at Ofgem) discovered after years of chasing numbers: "𝗗𝗼𝗻'𝘁 𝘁𝗿𝘂𝘀𝘁 𝗱𝗮𝘁𝗮 𝗯𝗹𝗶𝗻𝗱𝗹𝘆. 𝗧𝗿𝘂𝘀𝘁 𝘁𝗵𝗲 𝗰𝗼𝗺𝗯𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗵𝘂𝗺𝗮𝗻 𝘀𝘁𝗼𝗿𝗶𝗲𝘀." Because every dataset is a collection of human decisions - made under pressure and constraints. → "Low engagement" doesn't capture that the system made jobs harder, not easier → "95% training completion" misses that people clicked through just to finish, learning nothing → "30% faster processing" hides that staff now work unpaid overtime fixing new system errors The data is accurate. The story it tells? Completely wrong. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝘆 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲𝗻'𝘁 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗱 𝗯𝘆 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 𝗮𝗹𝗼𝗻𝗲. They're the ones where the human story and the data story actually match. --- 𝗙𝗿𝗼𝗺 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗧𝗩 - 𝗘𝗽𝗶𝘀𝗼𝗱𝗲 𝟯 Unfiltered stories. Ridiculous realities. Yes, as I said - Human. 😇 🎥 Watch the full conversation with Praveen Tomar - including his story about the 54 automations that were technically perfect but missed human purpose entirely: https://lnkd.in/eMC5d4AK What's your experience? When has data told you one story, but reality was completely different? ♻️ Repost if this resonates 👋 Follow me Rujuta Singh for more unfiltered transformation insights #humancenteredtransformation #transformationrealitytv #DigitalTransformation #solvedtogether

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🚨 India’s consumption puzzle: Why do two official datasets tell different stories about how we spend? PFCE (top-down; macro-consumption) vs. HCES (bottom-up; micro-consumption) 🔹 Gap has widened from 5% in 1972–73 to ~45% today. 🔹 Free/subsidised services (education, healthcare, meals) inflate PFCE but barely appear in HCES 🔹 Food share in household budgets is falling; non-food (transport, healthcare, rent, education) dominates This divergence isn’t just statistical—it affects how we measure poverty, prosperity, and the impact of policy. 📊 Latest piece by Ashish Kumar and Payal Seth from Pahlé India Foundation (PIF) in businessline unpacks the data divide and calls for urgent reforms (https://lnkd.in/dJWjyCQe)

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