15 Top Machine Learning Companies in Delhi

Delhi NCR has become one of India's most important technology regions. Gurugram and Noida host large analytics, consulting and engineering centres. New Delhi has a growing base of AI, cybersecurity and government-facing technology firms. Together they give businesses a deep pool of data science talent and a mix of global brands, Indian IT majors and focused startups.

Demand for machine learning companies in Delhi is rising because ML has moved from experiment to everyday tool. Banks use it to flag fraud, retailers to forecast demand, hospitals to analyse data, and customer service teams to automate conversations. Generative AI has widened this further by making language and image models accessible to non-technical teams.

This guide covers 15 companies with a meaningful Delhi/NCR presence and real ML or AI offerings. We separate AI-first specialists from broader IT firms that offer ML as one service line. The list is not a ranking and no company is called "the best", because the right partner depends on your goals, industry and data. After the list, you'll find practical guidance on choosing a partner and the trends shaping the market in 2026.

15 Top Machine Learning Companies in Delhi to Know in 2026

Each entry notes whether the firm is AI/analytics-first or a broader IT or consulting company.

1. Fractal Analytics

Website: https://fractal.ai

About the Company:
Fractal is an AI-first analytics firm headquartered in Mumbai, with offices in Gurugram. It helps large enterprises apply machine learning, generative AI and decision-science to problems in consumer goods, financial services, healthcare and technology. Its work spans predictive analytics, customer intelligence and AI engineering. Fractal is notable as one of India's best-known analytics specialists and a common partner for enterprise AI programmes.

2. Genpact

Website: https://www.genpact.com

About the Company:
Genpact is a global professional services firm headquartered in New York, with one of its largest Indian workforces in Gurugram and the wider NCR. It is a broader services company rather than a pure ML specialist, combining process expertise with data, analytics, and generative and agentic AI services. It serves banking, insurance, life sciences and consumer industries, and is notable for embedding AI inside large-scale business operations.

3. EXL

Website: https://www.exlservice.com

About the Company:
EXL is a data analytics and digital operations company headquartered in New York, with major delivery centres in Noida and Gurugram. Its AI offerings include predictive modelling, claims and underwriting analytics, and its EXLerate.AI platform for applying generative AI to enterprise workflows. It focuses on insurance, banking, healthcare and utilities, making it a strong fit for regulated industries needing analytics tied to operations.

4. Nagarro

Website: https://www.nagarro.com

About the Company:
Nagarro is a digital engineering company with global headquarters in Munich and its Indian base in Gurugram. It is primarily a software engineering firm, with data, AI and machine learning offered as part of broader digital product work. Clients span financial services, retail, manufacturing and travel. It suits businesses that want ML built into full applications and platforms rather than delivered as standalone analytics.

5. HCLTech

Website: https://www.hcltech.com

About the Company:
HCLTech is a global IT services company headquartered in Noida, making it one of the few firms on this list with a genuine NCR home base. It is a broad IT provider, with AI and ML offered across engineering, cloud and enterprise services, including its AI Force platform. Its large scale and industry coverage suit enterprises seeking ML as part of long-term transformation programmes.

6. Sprinklr

Website: https://www.sprinklr.com

About the Company:
Sprinklr is a customer experience software company headquartered in New York, with a large engineering presence in Gurugram. It is a product company rather than a consultancy, using AI to power social listening, customer service, marketing and insights across channels. It serves large brands that need to analyse customer conversations at scale, which makes it notable for applied NLP in a commercial software product.

7. Innovaccer

Website: https://innovaccer.com

About the Company:
Innovaccer is a healthcare technology company headquartered in San Francisco, with a significant engineering office in Noida. It builds a data and AI platform that unifies patient records and supports analytics, care management and automation for health systems and payers. It is a good example of vertical ML, where deep healthcare focus matters more than general-purpose AI services.

8. ZS

Website: https://www.zs.com

About the Company:
ZS is a management consulting and technology firm headquartered in Evanston, Illinois, with offices in the Delhi region. It is a consultancy with strong data science capability rather than a pure AI firm. ZS applies analytics, machine learning and AI to commercial and clinical problems, mainly in pharmaceuticals, biotech and healthcare. It is notable for combining domain expertise in life sciences with advanced analytics.

9. Evalueserve

Website: https://www.evalueserve.com

About the Company:
Evalueserve is a research, analytics and AI-led services firm with deep roots in the Gurugram area. It supports corporate and financial clients with data analytics, market intelligence and applied AI, including generative AI for research workflows. Its blend of analyst expertise and technology makes it relevant for businesses that need ML outputs interpreted for strategy and decision-making, not just models.

10. Staqu Technologies

Website: https://www.staqu.com

About the Company:
Staqu is a Gurugram-based AI company specialising in computer vision and video analytics. It builds AI systems that analyse visual and audio data for use cases such as safety monitoring, security and operational analytics, including its JARVIS video intelligence platform. It is an AI-first firm with a narrow, clear specialty, which makes it a useful reference point for businesses exploring vision-led ML applications.

11. Innefu Labs

Website: https://www.innefu.com

About the Company:
Innefu Labs is a New Delhi-headquartered cybersecurity and AI company. It develops analytics and security products, including its Prophecy platform for data analysis and investigation, along with AI-based authentication tools. It serves government agencies, law enforcement and enterprises. Innefu is notable as a Delhi-rooted product company where machine learning supports security and intelligence use cases rather than general business analytics.

12. Impetus Technologies

Website: https://www.impetus.com

About the Company:
Impetus Technologies is a data engineering and analytics company headquartered in the United States, with Indian delivery centres that include the Delhi NCR. It focuses on modern data platforms, cloud data migration, and analytics and AI engineering. Its strength lies in building the data foundations that machine learning depends on, which suits enterprises that need pipelines and infrastructure ready before model development.

13. Coforge

Website: https://www.coforge.com

About the Company:
Coforge is a global IT services company headquartered in Noida. It is a broader IT firm rather than an ML specialist, offering data, AI and automation services alongside software engineering and cloud work. It primarily serves travel, banking, insurance and financial services clients. It is worth considering for organisations wanting ML delivered alongside application modernisation by a company with a strong NCR base.

14. QuantumBlack, AI by McKinsey

Website: https://www.quantumblack.com

About the Company:
QuantumBlack is the AI and advanced analytics arm of McKinsey & Company, which has a large presence in Gurugram. It combines data scientists and engineers with consulting teams to build ML and generative AI solutions for large enterprises. It operates at the strategy-to-implementation end of the market, making it relevant to leadership teams planning enterprise-wide AI adoption rather than small, isolated projects.

15. Infogain (including Absolutdata)

Website: https://www.infogain.com

About the Company:
Infogain is a digital platforms and engineering company that acquired the analytics firm Absolutdata, which was originally based in the Gurugram area. It offers AI, data and analytics services alongside software engineering, so it is a broader technology firm with a notable analytics heritage. It serves retail, travel, healthcare and technology clients, and suits businesses that want analytics and engineering from one partner.

What Do Machine Learning Companies Do?

ML companies turn data into systems that learn from patterns and make predictions or decisions. Common services include:

• Predictive analytics: Models that estimate future outcomes, such as customer churn, equipment failure or loan default.
• Recommendation systems: Engines that suggest products, content or actions based on behaviour, widely used in e-commerce and media.
• Computer vision: Systems that interpret images and video for quality inspection, security, document processing or medical imaging.
• Natural language processing (NLP): Technology that understands text and speech, powering chatbots, sentiment analysis and document summarisation.
• Fraud detection: Models that spot unusual transactions or behaviour in real time for banks, insurers and payment firms.
• Customer analytics: Segmentation, lifetime value modelling and journey analysis that help teams understand what customers do and why.
• Forecasting: Time-series models for demand, inventory, revenue and workforce planning.
• Generative AI: Large models that create text, images or code, used in assistants, content workflows and knowledge search.
• ML model development: The end-to-end work of preparing data, training, validating, deploying and monitoring custom models.

Why Is Machine Learning Important in 2026?

• Automation: ML handles repetitive classification, extraction and routing tasks, freeing people for work that needs judgement.
• Predictive decision-making: Forecasts and risk scores let leaders act on likely outcomes instead of reacting to past reports.
• Personalization: Offers, content and pricing can be tailored to individual customers at scale.
• Customer insights: NLP and behavioural models reveal patterns in reviews, calls and usage data that manual analysis would miss.
• Fraud and risk detection: Models adapt to new fraud patterns faster than fixed rules alone.
• Operational efficiency: Predictive maintenance, route optimisation and demand planning reduce waste and downtime.
• Business forecasting: Better forecasts support smarter budgeting, staffing and procurement.
• Product innovation: ML enables features that were previously impractical, such as voice interfaces, smart search and AI copilots.

ML is not a cure-all, though. Results depend heavily on data quality, clear business goals and sound deployment.

How to Choose a Machine Learning Company in Delhi

• ML expertise and AI capabilities: Confirm the firm has hands-on experience with the techniques your problem needs, such as time-series forecasting, vision or language models. Ask what is built in-house versus assembled from third-party tools.
• Technical team: Ask about the data scientists, ML engineers and MLOps specialists who will actually work on your project, not just the sales team.
• Industry experience: A partner who understands your domain, whether finance, healthcare, retail or logistics, will need less onboarding and will avoid common pitfalls.
• Portfolio and case studies: Look for documented outcomes and ask for references. Treat vague claims without context with caution.
• Data security and compliance: Ask how data is stored, who can access it and how the firm handles privacy requirements, including India's data protection rules and any sector-specific regulation.
• Model development capabilities: Check how they handle data preparation, experimentation, validation, bias testing and documentation.
• Cloud and infrastructure expertise: Make sure they work with your cloud provider and can build reliable pipelines, not just prototypes.
• Scalability: A model that works in a pilot must also work in production, with real volumes and latency demands.
• Support and maintenance: Models degrade as data changes. Clarify who monitors performance, retrains models and responds to issues after launch.

Specialist or generalist? AI-first firms often move faster on novel ML problems. Larger IT and consulting firms can offer integration, governance and scale. Many projects benefit from a pilot with one partner before a bigger commitment.

Machine Learning Industry in Delhi in 2026

• Generative AI: Enterprises are moving from pilots to production use cases such as document search, customer support and content workflows.
• AI agents: Systems that plan and carry out multi-step tasks, such as handling a service request end to end, are a growing focus, along with the controls needed to keep them reliable.
• Predictive analytics: Still the backbone of ML work in banking, insurance, retail and logistics.
• Computer vision: Adoption continues in security, manufacturing quality checks and retail analytics.
• NLP and large language models: Demand is growing for systems that handle Indian languages and domain-specific content, along with approaches that ground model answers in company data.
• MLOps: As more models reach production, monitoring, versioning and automated retraining are becoming standard expectations.
• Edge AI: Running models on devices and local hardware helps where latency, cost or privacy matter.
• Automated machine learning (AutoML): Tools that speed up model building are making ML more accessible to smaller teams, though expert review remains important.
• AI-powered business solutions: More vendors are packaging ML into ready-to-use products for specific industries rather than only offering custom builds.

Final Thoughts

Delhi NCR offers a varied machine learning ecosystem, from AI-first analytics specialists to global consultancies and large IT services firms. That variety is useful, but it means the right choice is rarely obvious. Begin with your goal, whether that is reducing fraud, improving forecasts, automating support or building an AI product, and be honest about the state of your data.

Then look for a partner whose expertise, industry knowledge and delivery model match that goal. Ask for evidence, check security and support commitments, and consider a pilot before scaling. With a clear problem and a well-matched partner, ML can deliver practical value in 2026 and beyond.