Fractal Analytics
India-founded AI and analytics company that completed an NSE/BSE IPO in February 2026.
What is Fractal Analytics?
Fractal Analytics is a multinational AI and data analytics company founded in 2000 in Mumbai by Srikanth Velamakanni, Pranay Agrawal, Nirmal Palaparthi, Pradeep Suryanarayan, and Ramakrishna Reddy, with dual headquarters in Mumbai and New York. The company completed an initial public offering on India's National Stock Exchange and Bombay Stock Exchange in February 2026, becoming the first Indian AI company to go public, and reports roughly 5,000–6,900 employees across 18 global locations.
Fractal Analytics was founded in 2000 and is headquartered in Mumbai, India / New York, USA. The firm employs 5,001–10,000 people and works primarily with clients in Retail, Financial Services, Healthcare, Technology/SaaS sectors. Its primary differentiator is: First Indian AI company to complete an IPO (NSE/BSE, February 2026), adding public financial transparency.
Fractal Analytics tech stack and services
| Service area |
|---|
| Custom ML Development |
| Predictive Analytics |
| Data Engineering |
| AI Consulting |
Fractal Analytics use cases
Short answer: Fractal Analytics is best suited for large enterprises, publicly-listed AI/analytics partner.
| Use case |
|---|
| Enterprise AI and analytics transformation programs at global scale |
| Buyers who specifically want a publicly-listed AI vendor for procurement/compliance reasons |
| Multi-year analytics partnerships spanning retail, finance, and healthcare |
Fractal Analytics pricing
Short answer: Fractal Analytics uses a fixed project and managed analytics engagements pricing approach. Minimum engagement starts at Not published.
| Engagement model | Typical range | Best for |
|---|---|---|
| Fixed project | From Not published | Well-defined scope |
| Managed services | Variable; depends on team size | Large programmes or team augmentation |
Fractal Analytics pros and cons
| Advantages | Things to consider |
|---|---|
| +25 years of continuous operation, among the longest track records on this list | -Enterprise scale and public-company overhead can mean longer sales cycles than boutique competitors |
| +Public listing (NSE/BSE, Feb 2026) adds a level of financial disclosure most private competitors lack | -Broad analytics positioning means ML-specialist depth is one part of a wider data/AI portfolio |
| +5,000+ employees across 18 countries supports very large, globally distributed programs | -Minimum engagement size not publicly disclosed |
| +Founding team has remained core to the company since 2000 |
Fractal Analytics vs alternatives
How Fractal Analytics compares to the other top Machine Learning Development agencies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Neurons Lab | Regulated finance firms, PoC-to-production ML delivery. | One of the few AI consultancies worldwide holding AWS's Advanced Machine Learning Consulting Competence. | 4.8 | Full comparison |
| Tensorway | Mid-market companies, full-stack ML plus agentic AI. | full-stack ml delivery — data science, mlops, and llm/agentic frameworks (langchain, langgraph, autogen) — in one team. | 4.6 | Full comparison |
| Provectus | Mid-market and enterprise buyers, AI bundled with cloud... | Combines AI/ML delivery with cloud and big-data engineering as a single integrated systems-integrator practice. | 4.5 | Full comparison |
| InData Labs | Fintech, healthcare, SaaS — specialist data-science boutique. | Dedicated in-house R&D center focused specifically on data science and AI rather than broad software outsourcing. | 4.5 | Full comparison |
| AI Superior | EU SMBs, research-grade ML at accessible pricing. | PhD-founder-led team with an explicit research-and-development service line alongside standard client delivery. | 4.3 | Full comparison |
| Data Monsters | GPU-heavy deep learning, NVIDIA-partnered lab. | Elite NVIDIA partnership status supporting GPU-optimized deep learning delivery (per company website; independently unverifiable tier). | 4.2 | Full comparison |
| ITRex Group | Mid-market companies, AI/ML plus IoT/edge deployment. | Explicit focus on applied AI paired with intelligent-edge and IoT development, not just cloud-based ML. | 4.2 | Full comparison |
| Ideas2IT | Healthcare and BFSI enterprises, AI within product engineering. | Employee-ownership model paired with vertical focus in Healthcare, BFSI, and Manufacturing. | 4.1 | Full comparison |
| Quantiphi | Financial-services enterprises, cloud-native AI at scale. | AI-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist IT outsourcing. | 4.4 | Full comparison |
| Tredence | Retail, CPG, industrials — vertical-focused data science at... | Deep vertical focus applying AI specifically within retail, CPG, and industrials contexts rather than horizontal AI consulting. | 4.2 | Full comparison |
| Sigmoid | Large enterprises, data-engineering-first ML delivery. | Data-engineering-first delivery model, with ML/AI built directly on pipelines the firm also builds and manages. | 4.2 | Full comparison |
| LatentView Analytics | Companies wanting BI delivery with ML layered in. | Publicly listed (NSE/BSE since 2021) analytics firm with two decades of operating history. | 3.9 | Full comparison |
| Indium Software | Existing Indium QA clients adding AI/ML. | Long-standing QA and testing heritage now paired with proprietary AI accelerators like teX.ai. | 3.8 | Full comparison |
| Grid Dynamics | Enterprises needing SEC-level transparency, AI at scale. | Nasdaq-listed public company (GDYN) with SEC-filed financials, offering procurement transparency few competitors match. | 4.1 | Full comparison |
| Persistent Systems | Very large enterprises, AI from their existing IT... | Enterprise-wide scale (24,000+ employees) supporting AI/ML as part of a full IT services portfolio, not a standalone specialty. | 3.8 | Full comparison |
| EPAM Systems | Largest global enterprises, AI within a massive engineering... | Largest headcount on this list (62,000+) with NYSE-listed financial transparency and a proprietary LLM orchestration platform (EPAM DIAL). | 3.8 | Full comparison |
| SoftServe | Enterprises wanting an established AI/ML, cloud, and IoT... | 32 years of continuous operation spanning both a US public-market presence and deep Ukrainian engineering roots. | 4.0 | Full comparison |
| N-iX | Fortune 500 clients, European-HQ dedicated ML/AI line. | 23 years of operating history originating from a Novell technology acquisition, now serving Fortune 500 clients from a Malta-based HQ. | 4.0 | Full comparison |
| DataArt | Finance, media, healthcare enterprises — established global AI... | 28 years of operating history across 30+ global delivery locations, with a newer (2024) dedicated AI strategy consulting service line. | 3.9 | Full comparison |
| Andersen | Mid-to-large enterprises, AI/ML plus custom software, one vendor. | Named AI-powered robotic integration line alongside standard AI/ML and data science services. | 4.0 | Full comparison |
| Innowise Group | Companies wanting AI/ML within full-cycle software development. | Full-cycle software development scope (web, mobile, cloud, QA, security) with AI/ML as one of several integrated specialties. | 3.9 | Full comparison |
| Sigma Software Group | Companies wanting ML from a top-ranked outsourcing firm. | Consecutive annual placement on IAOP's World's Top 100 Outsourcing list every year since 2015. | 4.0 | Full comparison |
| Exadel | Enterprises, end-to-end model design through MLOps. | Explicit end-to-end scope 'from model design to MLOps and integration' as one of five named core service lines. | 4.1 | Full comparison |
| MobiDev | Retail, hospitality, fitness companies — proven mid-size AI... | 65+ delivered AI/ML products concentrated in retail, hospitality, fitness, and health/wellness verticals. | 4.2 | Full comparison |
| Master of Code Global | Companies building conversational AI and chatbot products. | Specialization narrowly focused on conversational AI and chatbots, with 1,000+ projects delivered over 21 years. | 4.1 | Full comparison |
| ScienceSoft | Companies wanting AI/ML from an established IT generalist. | 36 years of continuous IT consulting history, one of the longest track records among firms on this list. | 3.9 | Full comparison |
| Intellectsoft | Enterprises wanting AI app development, brand-name client history. | Named enterprise client roster (EY, Harley-Davidson, London Stock Exchange, Qualcomm, Jaguar) rare among mid-size firms on this list. | 4.0 | Full comparison |
| Belitsoft | Small-to-mid companies, affordable AI/ML add-on. | 21 years as a custom software development firm now expanding deliberately into generative AI and predictive analytics. | 3.9 | Full comparison |
| Neoteric | SMBs wanting an accessible generative-AI specialist. | 20 years of operating history condensed into a compact, generative-AI-focused team rather than a broad IT services portfolio. | 4.3 | Full comparison |
| Addepto | Companies wanting boutique AI/BI, now KMS-backed. | Boutique AI/BI consultancy that gained additional scale and resources through its December 2025 acquisition by KMS Technology. | 4.1 | Full comparison |
| Softweb Solutions | Companies needing AI/ML plus IoT, Avnet-backed. | Backed by Avnet, a global electronics distributor, giving unusual hardware/IoT supply-chain proximity for AI-on-device projects. | 3.9 | Full comparison |
Fractal Analytics FAQ
What is Fractal Analytics?
Fractal Analytics is a multinational AI and data analytics company founded in 2000 in Mumbai by Srikanth Velamakanni, Pranay Agrawal, Nirmal Palaparthi, Pradeep Suryanarayan, and Ramakrishna Reddy, with dual headquarters in Mumbai and New York. The company completed an initial public offering on India's National Stock Exchange and Bombay Stock Exchange in February 2026, becoming the first Indian AI company to go public, and reports roughly 5,000–6,900 employees across 18 global locations.
How much does Fractal Analytics charge?
Fractal Analytics uses fixed project and managed analytics engagements pricing. Minimum engagement starts at Not published. A discovery call is required to get project-specific quotes.
What tech stack does Fractal Analytics use?
Fractal Analytics works with Python, TensorFlow, PyTorch, AWS, Azure, Snowflake. Primary industries served include Retail, Financial Services, Healthcare, Technology/SaaS.
Is Fractal Analytics right for enterprise?
Large enterprises, publicly-listed AI/analytics partner. 5,001–10,000 team size. Key consideration: Enterprise scale and public-company overhead can mean longer sales cycles than boutique competitors.
What are the best Fractal Analytics alternatives?
The best alternatives to Fractal Analytics depend on your use case. Top options are:
- Neurons Lab: one of the few ai consultancies worldwide holding aws's advanced machine learning consulting competence.
- Tensorway: full-stack ml delivery — data science, mlops, and llm/agentic frameworks (langchain, langgraph, autogen) — in one team.
- Provectus: combines ai/ml delivery with cloud and big-data engineering as a single integrated systems-integrator practice.