| Event | Date |
|---|---|
| Open Date | 09-02-2026 , Monday |
| Close Date | 11-02-2026 , Wednesday |
| Tentative Allotment | 12-02-2026 , Thursday |
| Tentative Listing Date | 16-02-2026 , Monday |
| Retail Appl. Cut Off Time | 11-02-2026 , Wednesday 05:00 PM |
| Non Retail Appl. Cut Off Time | 11-02-2026 , Wednesday 04:00 PM |
| Anchor Allotment | 06-02-2026 , Friday |
| Initiation Of Refund | 13-02-2026 , Friday |
| Credit Of Share To Demat | 13-02-2026 , Friday |
Fractal Analytics Limited is a globally recognized enterprise Artificial Intelligence company founded in 2000. The company helps large global enterprises improve decision-making by using Artificial Intelligence, data analytics, engineering, design, domain knowledge and functional expertise.
The company’s vision is to power human decisions in enterprises by leveraging AI. In practical terms, Fractal helps clients understand customers, predict behavior, improve business operations, automate decision-making, create AI-led products and build enterprise-grade AI systems.
| Short Form | Full Form |
|---|---|
| AI | Artificial Intelligence |
| Gen AI | Generative Artificial Intelligence |
| LLM | Large Language Model |
| LLM Ops | Large Language Model Operations |
| ML | Machine Learning |
| ML Ops | Machine Learning Operations |
| NLP | Natural Language Processing |
| CPG | Consumer Packaged Goods |
| HLS | Healthcare and Life Sciences |
| TMT | Technology, Media and Telecom |
| BFSI | Banking, Financial Services and Insurance |
| MWC | Must Win Client |
| NPS | Net Promoter Score |
| R&D | Research and Development |
| RAI | Responsible Artificial Intelligence |
| AGI | Artificial General Intelligence |
| B2C | Business-to-Consumer |
As of September 30, 2025, Fractal’s AI solutions are organized under two major segments: Fractal.ai and Fractal Alpha. These segments allow the company to serve enterprise clients through AI services, AI products and AI businesses.
| Segment | Description | IPORupee Interpretation |
|---|---|---|
| Fractal.ai | Includes AI services and AI products, primarily hosted on Cogentiq. | This is the core enterprise AI engine of the company. |
| Fractal Alpha | Includes AI businesses such as Analytics Vidhya, Asper and Qure.ai as an associate company. | This shows that Fractal is also creating AI-led businesses and platforms beyond services. |
Fractal helps clients solve real business problems through AI. Its work is not limited to dashboards or reports. The company focuses on business decisions, operational efficiency, customer behavior, product innovation and executive decision-making.
| Business Area | Example of Solution |
|---|---|
| Customer behavior | Uses Gen AI to build customer strategy and customer intelligence systems. |
| Operational efficiency | Builds Gen AI-powered call auditing, summarization and escalation identification solutions. |
| Product development | Uses AI and consumer data to identify growth opportunities and unmet customer needs. |
| Sustainability and resilience | Uses Gen AI to identify product concepts, opportunities and customer pain points. |
| Executive decision-making | Builds AI, data and analytics infrastructure for business reviews, forecasting and growth experiments. |
Fractal’s business is built around three important capability pillars: Artificial Intelligence, Engineering and Design. This combination is important because enterprise AI requires more than model development. It requires data readiness, integration, governance, user adoption and measurable business impact.
| Capability | Meaning |
|---|---|
| Algorithmic Decision Making / Core ML | Uses data to generate scores, predictions and probabilities for business decisions. |
| Machine Vision | Extracts relevant information from images and visual data. |
| Conversational AI / NLP | Enables machines to understand and respond to human language. |
| ML Ops | Helps deploy, monitor and maintain machine learning models in real-world business use. |
| Gen AI | Creates new content such as text, images or business outputs. |
| LLM Ops | Manages Large Language Models in commercial products and enterprise environments. |
| Capability | Meaning |
|---|---|
| Cloud Engineering and Migration | Helps companies modernize data infrastructure on cloud platforms. |
| Data Platforms | Builds scalable platforms for structured, semi-structured and unstructured data. |
| Platform Ops | Builds enterprise-grade platforms with reliability, observability and DevSecOps. |
| Data Governance | Creates secure and trusted data systems governed by appropriate policies. |
| Decision Systems | Builds business intelligence tools, dashboards, conversational AI and web/mobile apps. |
| Next Gen Software Development | Develops AI-led tools for cloud migration, platform modernization and enterprise applications. |
| Capability | Meaning |
|---|---|
| Behavioral Sciences | Studies human decision behavior to design better AI solutions. |
| Design Research | Uses research methods to understand user needs and improve adoption. |
| User Experience and Interaction Design | Designs interfaces and AI workflows that are easier for enterprise users to use. |
Cogentiq is Fractal’s agentic AI platform for enterprises. It is designed to help enterprises unlock efficiency through AI agents, tools, connectors and multi-agent suites.
| Layer | What It Includes |
|---|---|
| Data Sources / Foundation | Structured data, unstructured data, data lakes, documents, images and enterprise APIs. |
| Tool Marketplace | Advanced RAG, text-to-SQL, text-to-graph, multimodal RAG, forecasting, pricing and driver analysis. |
| Agent Marketplace | Customer service agents, sales agents and marketing agents. |
| Multi-Agent Suite | Customer Experience Suite, Business Insight, Supply Chain Suite, Financial Reporting Suite, Sales Enablement and IT Support Suite. |
| Product | Description |
|---|---|
| MarshallGoldsmith.ai | Virtual business coach based on Marshall Goldsmith’s philosophy. |
| Kalaido.ai | Gen AI-powered text-to-image generator. |
| Vaidya.ai | Gen AI-powered multimodal health assistant. |
| Industry | Revenue | Share |
|---|---|---|
| CPG and Retail | Rs. 5,692 million | 37.5% |
| TMT | Rs. 4,134 million | 27.2% |
| HLS | Rs. 2,581 million | 17.0% |
| BFSI | Rs. 1,856 million | 12.2% |
| Others | Rs. 921 million | 6.1% |
| Total Fractal.ai Revenue | Rs. 15,184 million | 100.0% |
| Industry | Revenue | Share |
|---|---|---|
| CPG and Retail | Rs. 10,615 million | 39.3% |
| TMT | Rs. 8,087 million | 29.9% |
| HLS | Rs. 3,745 million | 13.8% |
| BFSI | Rs. 2,980 million | 11.0% |
| Others | Rs. 1,610 million | 6.0% |
| Total Fractal.ai Revenue | Rs. 27,037 million | 100.0% |
Fractal works with marquee clients across CPG, TMT, HLS and BFSI. The RHP mentions clients such as Citi, Costco, Franklin Templeton, Mars, Mondelez, Nationwide, Nestle and Philips.
| Particulars | Six Months Ended September 30, 2025 | Fiscal 2025 |
|---|---|---|
| Must Win Clients | 122 | 113 |
| Revenue from Top 10 Clients | 54.2% | 53.8% |
| Net Revenue Retention in Fractal.ai Segment | 114.0% | 121.3% |
| Clients generating at least US$1 million revenue | 52 | 53 |
| Net Promoter Score | 76 | 77 |
Fractal has consistently invested in R&D and AI innovation. The company’s R&D journey includes Fractal Sciences, ai.now, Gen AI solutions, acquisitions, Project Ramanujan, Fathom-R1-14B and IndiaAI Mission participation.
| Particulars | Six Months Ended September 30, 2025 | Fiscal 2025 | Fiscal 2024 | Fiscal 2023 |
|---|---|---|---|---|
| Research and Development Investment | Rs. 956 million | Rs. 1,436 million | Rs. 1,422 million | Rs. 1,158 million |
| R&D as % of Revenue from Operations | 6.1% | 5.2% | 6.5% | 5.8% |
| Particulars | Registered | Pending |
|---|---|---|
| Patents | 28 | 38 |
| Trademarks | 376 | 104 |
The company owns trademark registrations in India for brands such as Concordia, Customer Genomics, Fractal, Fractal Analytics and Fractal.ai. It also owns copyright and patent registrations connected with technologies such as Alligator, Blisk, Nexus and Unif, along with AI products such as Concordia, Crux Intelligence, Customer Genomics and Cogentiq CX.
Fractal has a Responsible AI framework and policy guidelines. This is important because large enterprises are increasingly cautious about AI safety, fairness, bias, privacy, governance and explainability.
| Principle | Meaning |
|---|---|
| Social Well-being and Planet-inclusive | AI should create a net positive impact for humans, society and the planet. |
| Privacy and Safety | Personal and enterprise information should be protected. |
| Fairness and Equity | AI should reduce bias and support fair outcomes. |
| Robustness and Stability | AI systems should work reliably under different conditions. |
| Accountability | Humans should remain accountable for AI governance. |
| Transparency | AI systems should be understandable and explainable. |
Top 10 clients contributed 54.2% of revenue for the six months ended September 30, 2025 and 53.8% for Fiscal 2025. This means a large portion of revenue depends on a limited number of major clients.
The company operates in a fast-moving AI market where global technology companies, consulting firms, cloud providers and niche AI start-ups are also active.
Fractal has several products and platforms, but investors should monitor how much revenue comes from scalable products versus customized enterprise services.
AI businesses depend heavily on skilled talent. Retaining engineers, AI researchers, data scientists and enterprise delivery teams is critical.
Fractal Analytics Limited is a rare enterprise AI-focused IPO theme. The company is not just offering basic analytics services. It is building a broader AI ecosystem through enterprise AI services, AI products, AI research, Gen AI models, agentic AI platforms and AI businesses.
From a business quality perspective, the company has several attractive features: long operating history, marquee global clients, diversified industry exposure, strong Net Revenue Retention, high NPS score, R&D-led culture and growing AI product capability.
The company’s positioning becomes stronger because enterprise clients are not only looking for AI models. They need AI solutions that are secure, explainable, integrated with existing systems and usable by business teams. Fractal’s combination of AI, engineering, design and domain expertise is important in this context.
However, Fractal should not be evaluated only based on the AI theme. AI is a powerful narrative, but retail investors should focus on hard business indicators such as revenue growth, profitability, client concentration, cash generation, margin profile, product revenue contribution, R&D productivity and valuation.
The company started as an analytics and decision-support company but has evolved into an enterprise AI player. Its current positioning includes Gen AI, agentic AI, responsible AI, reasoning models and AI product platforms.
Cogentiq is one of the most important parts of Fractal’s business story. It provides an enterprise agentic AI platform with data connectors, tools, agent marketplace and multi-agent suites. If enterprise adoption improves, Cogentiq can help Fractal create more repeatable solutions, improve delivery efficiency and potentially improve margins.
Net Revenue Retention above 100% shows clients are spending more over time. This is a strong business signal. At the same time, top 10 clients contributing more than 50% of revenue means client concentration is high.
Fractal’s R&D spending and innovation history show that it is investing ahead of the curve. But retail investors should remember that R&D is valuable only when it creates business outcomes such as revenue growth, client wins, stronger products and better profitability.
For enterprises, AI adoption is not only about performance. It is also about safety, explainability, compliance, privacy and governance. Fractal’s Responsible AI framework may help it gain trust among large clients, especially in BFSI, healthcare and consumer data-heavy industries.
AI companies may receive premium valuations due to growth expectations. Retail investors should avoid judging the IPO only from the AI theme. The right approach is to compare valuation with growth, margins, client quality, product monetization and future scalability.
| Area | Why It Matters |
|---|---|
| Revenue Growth | Shows market demand and execution capability. |
| Client Concentration | Shows dependency on large clients. |
| Net Revenue Retention | Shows expansion within existing clients. |
| R&D Spending | Shows investment in future capabilities. |
| Product Revenue | Shows whether the business is becoming scalable. |
| Margins | Shows operating efficiency. |
| Cash Flow | Shows real business strength. |
| Valuation | Must be compared with growth and quality. |
Fractal Analytics Limited represents a high-quality enterprise AI theme with long operating history, marquee global clients, strong R&D focus, AI product platforms and responsible AI framework.
The company’s biggest strengths are enterprise client relationships, AI-led capabilities, Net Revenue Retention, research culture and platform potential through Cogentiq. The main areas to watch are client concentration, competition, product monetization, R&D payback, talent dependency and valuation comfort.
| Category | Percentage | No. of Shares Offered | Amount |
|---|---|---|---|
QIB | 75.00 % | 2,31,15,834 | 2,080.43 Cr |
Retail | 10.00 % | 30,82,110 | 277.39 Cr |
Total HNI | 15.00 % | 46,23,167 | 416.09 Cr |
SHNI | 5.00 % | 15,41,056 | 138.70 Cr |
BHNI | 10.00 % | 30,82,111 | 277.39 Cr |
Employee | 0.00 % | 7,36,196 | 60.00 Cr |
Fractal Analytics Ltd allotted 1,38,69,499 equity shares to anchor investors at ₹900 per share on 06 Feb 2026 before the IPO opening. The total anchor allocation stood at 1,248.25 Cr across 52 anchor investors.
Mutual funds received 52,77,680 shares worth 474.99 Cr, representing 38.05% of the total anchor investor allocation. Within the mutual fund portion, SBI Mutual Fund had the highest fund-house level allocation with 11,66,656 shares worth 105.00 Cr across 1 scheme. Other leading fund houses by allocation included ICICI Prudential Mutual Fund, Motilal Oswal Mutual Fund, UTI Mutual Fund and Baroda BNP Paribas Mutual Fund.
At scheme level, the largest mutual fund allocations included SBI SMALL CAP FUND, ICICI PRUDENTIAL TECHNOLOGY FUND, MOTILAL OSWAL MULTI CAP FUND, BARODA BNP PARIBAS MULTI CAP FUND and BANDHAN SMALL CAP FUND.
The largest anchor investor received 8.41% of the anchor portion. The top five anchor investors together received 47,22,192 shares, representing about 34.05% of the total anchor allocation.
| Rank | Anchor Investor Name | Shares Allocated | Allocation % | Allocation Amount |
|---|---|---|---|---|
| 1 | SBI SMALL CAP FUND | 11,66,656 | 8.41 % | 105.00 Cr |
| 2 | LIFE INSURANCE CORPORATION OF INDIA | 11,11,104 | 8.01 % | 100.00 Cr |
| 3 | ICICI PRUDENTIAL TECHNOLOGY FUND | 8,88,880 | 6.41 % | 80.00 Cr |
| 4 | AMUNDI FUNDS NEW SILK ROAD | 7,77,776 | 5.61 % | 70.00 Cr |
| 5 | GOLDMAN SACHS FUNDS - GOLDMAN SACHS INDIA EQUITY PORTFOLIO | 7,77,776 | 5.61 % | 70.00 Cr |
| Rank | Mutual Fund Scheme | AMC | Shares Allocated | Allocation % | Allocation Amount |
|---|---|---|---|---|---|
| 1 | SBI SMALL CAP FUND | SBI Mutual Fund | 11,66,656 | 8.41 % | 105.00 Cr |
| 2 | ICICI PRUDENTIAL TECHNOLOGY FUND | ICICI Prudential Mutual Fund | 8,88,880 | 6.41 % | 80.00 Cr |
| 3 | MOTILAL OSWAL MULTI CAP FUND | Motilal Oswal Mutual Fund | 3,88,864 | 2.80 % | 35.00 Cr |
| 4 | BARODA BNP PARIBAS MULTI CAP FUND | Baroda BNP Paribas Mutual Fund | 3,88,864 | 2.80 % | 35.00 Cr |
| 5 | BANDHAN SMALL CAP FUND | Bandhan Mutual Fund | 2,77,776 | 2.00 % | 25.00 Cr |
The figures are based on the company’s stock exchange anchor allotment intimation. Anchor allocation is only an informational disclosure and does not indicate future listing performance.
| Application | Discount | Qty (Lot) | Total |
|---|---|---|---|
Retail MIN | - | 16 (1) | ₹ 14,400 |
Retail MAX | - | 208 (13) | ₹ 1,87,200 |
SHNI MIN | - | 224 (14) | ₹ 2,01,600 |
SHNI MAX | - | 1104 (69) | ₹ 9,93,600 |
BHNI MIN | - | 1120 (70) | ₹ 10,08,000 |
Employee MIN | ₹ 85 | 16 (1) | ₹ 13,040 |
Employee MAX | ₹ 85 | 608 (38) | ₹ 4,95,520 |
| Particulars | Sept 30, 2025 | Sept 30, 2024 | Mar 30, 2025 | Mar 30, 2024 | Mar 30, 2023 |
|---|---|---|---|---|---|
Income | 1,594.30 | 1,322.60 | 2,816.20 | 2,241.90 | 2,043.70 |
Employee benefits expense | 1,125.20 | 971.70 | 2,004.80 | 1,737.00 | 1,608.50 |
Employee stock option expense | 27.00 | 41.80 | 79.80 | 96.30 | 158.70 |
Finance costs | 23.30 | 36.70 | 57.70 | 44.50 | 45.30 |
Depreciation and amortisation expense | 63.50 | 44.20 | 102.30 | 83.20 | 78.10 |
Other expenses | 207.20 | 153.70 | 330.90 | 289.60 | 334.60 |
Total Expenses | 1,446.20 | 1,248.10 | 2,575.50 | 2,250.60 | 2,225.20 |
Share of (loss) of an associate | -44.50 | -21.50 | -29.70 | -16.30 | -29.00 |
Exceptional items (loss) /gain | -4.80 | -2.90 | 27.00 | -5.50 | 523.90 |
Profit / (Loss) before tax expens | 98.80 | 50.10 | 238.00 | -30.50 | 313.40 |
Tax expense | 27.90 | -22.80 | 17.40 | 24.20 | 119.00 |
Profit / (Loss) for the period | 70.90 | 72.90 | 220.60 | -54.70 | 194.40 |
Basic EPS | 4.55 | 4.92 | 14.49 | -3.12 | 13.39 |
Diluted EPS | 4.09 | 4.52 | 13.36 | -3.12 | 12.42 |
| Particulars | Sept 30, 2025 | Sept 30, 2024 | Mar 30, 2025 | Mar 30, 2024 | Mar 30, 2023 |
|---|---|---|---|---|---|
Property, plant and equipment | 64.90 | 24.10 | 31.60 | 22.70 | 41.20 |
Right-of-use assets | 141.10 | 109.00 | 160.20 | 116.60 | 46.80 |
Goodwill | 369.00 | 352.50 | 358.20 | 351.30 | 347.50 |
Other intangible assets | 158.70 | 123.00 | 137.00 | 135.60 | 122.90 |
Intangible assets under development | 35.40 | 4.60 | 13.70 | 5.90 | 0.70 |
Investment accounted for using the equity method | 382.00 | 406.50 | 425.80 | 425.90 | 447.90 |
Financial assets | 37.70 | 31.90 | 25.40 | 25.20 | 27.90 |
Deferred tax assets (net) | 62.10 | 57.50 | 56.10 | 47.90 | 39.90 |
Income tax assets (net) | 27.50 | 23.40 | 18.80 | 19.30 | 16.20 |
Other non-current assets | 1.00 | 0.70 | 6.20 | 1.20 | 4.00 |
Total non-current assets (A) | 1,279.40 | 1,133.20 | 1,233.00 | 1,151.60 | 1,095.00 |
Investments | 671.70 | 335.60 | 561.40 | 445.50 | 290.60 |
Trade receivables | 620.00 | 581.80 | 584.80 | 533.30 | 500.90 |
Cash and cash equivalents | 110.20 | 140.20 | 564.90 | 81.20 | 213.20 |
Bank balances other than (iii) above | 0.30 | 11.30 | 23.40 | 6.60 | 7.10 |
Loans | 32.30 | 29.00 | 30.30 | 28.20 | 26.90 |
Other financial assets | 2.00 | 4.60 | 3.90 | 6.50 | - |
Other current assets | 249.50 | 207.40 | 155.90 | 139.10 | 115.00 |
Total current assets (B) | 1,686.00 | 1,309.90 | 1,624.60 | 1,240.40 | 1,153.70 |
Total assets (A+B) | 2,965.40 | 2,443.10 | 2,857.60 | 2,392.00 | 2,248.70 |
Equity Share Capital | 14.20 | 3.10 | 3.10 | 3.10 | 3.10 |
Other Equity | 1,958.40 | 1,541.50 | 1,750.10 | 1,402.60 | 1,340.00 |
Non-controlling interest | 6.90 | 11.30 | 12.20 | 14.20 | 20.30 |
Total equity (C) | 1,979.50 | 1,555.90 | 1,765.40 | 1,419.90 | 1,363.40 |
Borrowings | 263.90 | 256.30 | 257.70 | 250.10 | 322.10 |
Lease liabilities | 110.20 | 86.50 | 127.20 | 91.30 | 24.30 |
Other financial liabilities | 45.20 | 41.00 | 45.00 | 31.00 | 14.00 |
Provisions | 18.50 | 27.20 | 18.80 | 18.70 | 11.80 |
Deferred tax liabilities | 68.80 | 64.60 | 68.80 | 101.60 | 101.60 |
Total non-current liabilities (E) | 506.60 | 475.60 | 517.50 | 492.70 | 473.80 |
Borrowings | 10.70 | 5.90 | 8.50 | 0.00 | 3.50 |
Lease liabilities | 38.20 | 22.00 | 35.60 | 21.80 | 27.30 |
Trade payables | 82.90 | 73.10 | 62.00 | 51.20 | 57.10 |
Other financial liabilities | 203.40 | 162.80 | 291.30 | 245.40 | 186.60 |
Other current liabilities | 124.20 | 124.80 | 164.70 | 140.80 | 124.20 |
Provisions | 5.30 | 9.20 | 6.40 | 14.80 | 11.00 |
Current tax liabilities (net) | 14.60 | 13.80 | 6.20 | 5.40 | 1.80 |
Total current liabilities (F) | 479.30 | 411.60 | 574.70 | 479.40 | 411.50 |
Total liabilities (D=E+F) | 985.90 | 887.20 | 1,092.20 | 972.10 | 885.30 |
Total equity and liabilities (C+D) | 2,965.40 | 2,443.10 | 2,857.60 | 2,392.00 | 2,248.70 |
| Particulars | Sept 30, 2025 | Sept 30, 2024 | Mar 30, 2025 | Mar 30, 2024 | Mar 30, 2023 |
|---|---|---|---|---|---|
Cash Flow From Operating Activities | -21.4 | 7.9 | 397 | 159.5 | -30.6 |
Cash Flow From Investing Activities | -202.1 | 64.6 | -181 | -150.1 | 124.9 |
Cash Flow From Financing Activities | 59.3 | -14.6 | -22.4 | -145 | -57.4 |
Net Cash Flow | -164.2 | 57.9 | 193.6 | -135.6 | 36.9 |
| Objective | No Of Shares | Amount |
|---|---|---|
Fresh Issue | - | 1,023.50 Cr |
Offer for Sale | - | 1,810.40 Cr |
| Name | Contact Person | Telephone | Website | |
|---|---|---|---|---|
Kotak Mahindra Capital Company Limited | Ganesh Rane | +91 22 4336 0000 | fractal.ipo@kotak.com | www.investmentbank.kotak.com |
Axis Capital Limited | Mayuri Arya | +91 22 4325 2183 | fractal.ipo@axiscap.in | www.axiscapital.co.in |
Morgan Stanley India Company Private Limited | Sumit Kumar Agarwal | +91 22 6118 1000 | fractalipo@morganstanley.com | www.morganstanley.com |
Goldman Sachs (India) Securities Private Limited | Saurav S / Srishti Srivastava | +91 22 6616 9000 | fractalipo@gs.com | www.goldmansachs.com |
Understand company details, IPO size, price band, lot size, exchange listing, fresh issue and Offer for Sale (OFS) in simple language for retail investors.
This IPO is of Fractal Analytics Ltd. The issue is scheduled to open on Monday, 09 February 2026 and closes on Wednesday, 11 February 2026.
After the IPO process is completed, the shares are proposed to be listed on both NSE and BSE.
IPO size means the total amount offered to investors through the public issue.
For Fractal Analytics Ltd IPO, the total issue size is Rs 2,833.9 crore, consisting of a fresh issue of Rs 1,023.5 crore and an Offer for Sale (OFS) of Rs 1,810.4 crore.
Price band is the price range within which investors can bid for shares in a book-built IPO.
For this IPO, the lower price band is Rs 857 per share and the upper price band is Rs 900 per share.
Lot size is the minimum number of shares an investor must apply for in an IPO. IPO applications are usually made in fixed lot multiples.
For Fractal Analytics Ltd IPO, the minimum lot size is 16 shares. At the upper price band of Rs 900 per share, the minimum application amount is Rs 14,400. Applications must be made in multiples of 16 shares.
Fresh Issue means the company issues new shares to investors and receives money from that part of the IPO.
For Fractal Analytics Ltd IPO, the Fresh Issue size is Rs 1,023.5 crore. The company can use these funds for purposes mentioned in the IPO documents, such as business growth, repayment of borrowings, working capital, capital expenditure, or general corporate purposes.
Offer for Sale (OFS) means existing shareholders sell their shares to the public through the IPO.
For Fractal Analytics Ltd IPO, the Offer for Sale (OFS) size is Rs 1,810.4 crore. In an OFS, the selling shareholders receive the money, and the company usually does not receive funds from that portion.
Understand the important IPO dates, allotment process, refund schedule and listing timeline in a retail-friendly format.
The IPO opening date is the first day on which investors can apply for the public issue.
For Fractal Analytics Ltd IPO, the IPO opens on Monday, 09 February 2026.
The IPO closing date is the last day on which investors can submit, modify, or cancel their IPO applications.
Fractal Analytics Ltd IPO closes on Wednesday, 11 February 2026.
The tentative allotment date is the expected date on which the registrar finalizes the share allotment for valid IPO applications.
For Fractal Analytics Ltd IPO, the tentative allotment date is Thursday, 12 February 2026.
The tentative listing date is the expected date on which IPO shares begin trading on the stock exchanges.
Fractal Analytics Ltd IPO is expected to list on Monday, 16 February 2026.
After allotment, funds are unblocked or refunds are initiated for non-allotted investors, while allotted shares are credited to investors' demat accounts before listing.
For Fractal Analytics Ltd IPO, refund initiation is expected on Friday, 13 February 2026, and credit of shares to demat accounts is expected on Friday, 13 February 2026.
Understand IPO category-wise reservation, QIB quota, retail quota, HNI allocation, shareholder reservation and the meaning of structure columns in a simple format.
IPO structure shows how shares are divided among different categories of investors, such as QIB, Retail, NII/HNI, Employees, and Shareholders.
For Fractal Analytics Ltd IPO, the available categories in the structure table include QIB, Retail, Non-Institutional Investors (NII/HNI), Employees. The NII/HNI category includes SHNI and BHNI sub-categories. Investors can use this table to understand category-wise allocation before applying.
QIB stands for Qualified Institutional Buyers. This category includes mutual funds, banks, insurance companies, and other large financial institutions.
In Fractal Analytics Ltd IPO, 75% shares are reserved under the QIB category, with 2,31,15,834 shares offered, and an allocation amount of ₹2,080.43 crore.
The Retail category is reserved for individual investors and HUFs applying for up to Rs 2 lakh within the retail investment limit allowed under IPO rules.
In Fractal Analytics Ltd IPO, 10% shares are reserved under the Retail category, with 30,82,110 shares offered, and an allocation amount of ₹277.39 crore.
NII/HNI stands for Non-Institutional Investors / High Net-Worth Individuals. This category generally includes investors applying for more than Rs 2 lakh, above the retail investment limit.
In Fractal Analytics Ltd IPO, 15% shares are reserved under the NII/HNI category, with 46,23,167 shares offered, and an allocation amount of ₹416.09 crore.
SHNI stands for Small HNI. It is a sub-category within the NII/HNI category and generally refers to applications for more than Rs 2 lakh but not exceeding Rs 10 lakh.
In Fractal Analytics Ltd IPO, 5% shares are reserved under the SHNI sub-category, with 15,41,056 shares offered, and an allocation amount of ₹138.70 crore.
BHNI stands for Big HNI. It is a sub-category within the NII/HNI category and generally refers to applications for more than Rs 10 lakh.
In Fractal Analytics Ltd IPO, 10% shares are reserved under the BHNI sub-category, with 30,82,111 shares offered, and an allocation amount of ₹277.39 crore.
Employee reservation is a special quota reserved for eligible company employees, if applicable.
In Fractal Analytics Ltd IPO, 7,36,196 shares are offered under the Employee Reservation category, and an allocation amount of ₹60.00 crore.
Understand retail lot size, HNI application limits, employee category, shareholder category and investment amount calculations using the IPO lot table.
Lot size is the minimum number of shares an investor must apply for in an IPO. IPO applications are usually made in fixed lot multiples.
For Fractal Analytics Ltd IPO, the minimum lot size is 16 shares. At the upper price band of Rs 900 per share, the minimum application amount is Rs 14,400. Applications must be made in multiples of 16 shares.
Retail minimum application shows the minimum application size for retail investors.
For Fractal Analytics Ltd IPO, the Retail minimum application is 16 shares (1 lot) for about Rs 14,400.
Retail maximum application shows the maximum application size generally available under the retail category.
For Fractal Analytics Ltd IPO, the Retail maximum application is 208 shares (13 lots) for about Rs 1,87,200.
SHNI minimum application shows the minimum application size for the Small HNI category.
For Fractal Analytics Ltd IPO, the SHNI minimum application is 224 shares (14 lots) for about Rs 2,01,600.
SHNI maximum application shows the maximum application size available under the Small HNI category, when provided.
For Fractal Analytics Ltd IPO, the SHNI maximum application is 1,104 shares (69 lots) for about Rs 9,93,600.
BHNI minimum application shows the minimum application size for the Big HNI category.
For Fractal Analytics Ltd IPO, the BHNI minimum application is 1,120 shares (70 lots) for about Rs 10,08,000.
Employee minimum application shows the minimum application size under the employee category, if this category is available in the IPO.
For Fractal Analytics Ltd IPO, the Employee minimum application is 16 shares (1 lot) for about Rs 13,040 with a discount of Rs 85 per share.
Employee maximum application shows the maximum application size under the employee category, if provided in the lot size table.
For Fractal Analytics Ltd IPO, the Employee maximum application is 608 shares (38 lots) for about Rs 4,95,520 with a discount of Rs 85 per share.
Understand important financial figures in simple language using the financial statement data available for the IPO.
Financial Highlights show important numbers from the company's financial statements. These may include income, expenses, profit, assets, liabilities, cash flow and other financial information, depending on the data available for the IPO.
The table displays available financial data based on the IPO information currently available. Fields may differ from company to company depending on disclosures and reporting format.
Investors should read the table to understand the company's financial performance and financial position over different periods.
The table may help users review income, expenses, profitability, balance sheet position, cash movement or other financial information, depending on the available data.
Financial statement line items may differ because companies operate in different industries and may follow different reporting formats.
Some companies may provide detailed financial breakup, while others may present broader financial categories.
Investors should review income, expenses, profitability, debt position, asset base, liabilities and cash flow position together.
A single financial figure should not be used alone to judge the company.
Profit is important, but it does not show the full financial picture.
Investors should also review revenue quality, expenses, debt, assets, liabilities, cash flow, valuation and business risks.
Cash flow helps investors understand how money moves in and out of the business.
A company may report profit but still face cash flow pressure, so cash flow should be reviewed along with profit, debt and balance sheet information.
No. Financial Highlights are useful, but investors should also review valuation, peer comparison, KPI data, business risks, IPO pricing, management discussion and official offer documents such as the RHP or DRHP.
Understand how the company may be compared with similar businesses using the peer comparison data available for the IPO.
Peer Comparison means comparing the IPO company with businesses operating in a similar sector or industry.
It helps investors understand the company in a broader industry context.
Peer Comparison helps investors understand how the company can be compared with similar businesses using available financial or valuation metrics.
It is useful for context, but it should not be treated as a final investment conclusion.
Peer comparison metrics may differ depending on the companies selected, accounting format, business model and available public information.
Investors should compare only relevant and similar metrics.
Investors should use the peer comparison table as a reference point.
Available metrics should be read together with business model, scale, profitability, margins, debt, growth, valuation and IPO pricing.
No. Peer Comparison does not directly decide whether an IPO is good or bad.
It only provides context for comparison. Final analysis should include financial statements, KPI data, valuation, risk factors and official offer documents.
No. Peer Comparison is only one part of IPO analysis.
Investors should also review financial statements, KPI data, business risks, valuation, IPO pricing and company fundamentals.
Understand important KPI metrics in simple language using the key performance indicator data available for the IPO.
Key Performance Indicators, or KPIs, are financial and valuation metrics used to understand profitability, efficiency, leverage, return-based ratios and earnings performance.
They help investors understand the company beyond basic financial figures.
KPIs help investors assess profitability, capital efficiency, leverage, valuation and earnings quality.
They should be reviewed together with financial statements, peer comparison and IPO pricing.
KPI data may differ depending on the company, industry, financial disclosures and available offer document information.
Not every IPO may provide every KPI, and some metrics may not be applicable to every business.
Investors should read KPI metrics together instead of relying on one ratio.
A single KPI may not give the full picture unless it is reviewed with financial statements, peer comparison, business model and valuation.
No. High or low KPI values need context.
The meaning of a ratio may differ depending on industry, business model, debt level, growth stage, profitability and IPO valuation.
No. KPI data is useful, but it should not be used alone.
Investors should also review financial statements, peer comparison, business risks, valuation, IPO pricing and official offer documents such as the RHP or DRHP.