14 Innovative AI Startups in India Shaping the Future of Technology

Did you know India ranks second in public generative AI projects on GitHub? The country is home to 16% of the world’s AI talent, showcasing its growing influence in AI innovation and adoption. AI startups in India are now shaping India's economy with their influence in the telecom, finance, and healthcare sectors.

In 2025, some of the leading AI startups in India are Niramai Health Analytix, Mad Street Den, Arya.ai, and more. Increasing investments, a growing talent pool, and the startup culture in India support the rise with the onset of venture capital investments and government initiatives. 

These companies use technologies such as computer vision, deep learning, predictive analytics, and more to build high-performing AI platforms. Tools like Pytorch and Tensorflow allow them to stand out on their platforms and gain global attention. 

In this blog, we will explore some of the top AI startups in India that are playing a significant role in sectors like telecom, manufacturing, edtech, finance, and healthcare.

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14 Leading AI Startups in India Revolutionizing the Tech Sector

India’s AI innovation is expanding by applying deep learning, computer vision, natural language processing (NLP), and predictive analytics across industries. AI startups in India are automating legacy processes, designing scalable multi-cloud AI platforms, and edge AI in healthcare, logistics, and customer services. 

If you want to learn all the fundamentals before opening an AI startup in India, the following courses from upGrad can help you. 

• Executive Diploma in Machine Learning and AI with IIIT-B
• Master’s Degree in Artificial Intelligence and Data Science
• Generative AI Foundations Certificate Program

Below is a brief overview of some of the leading AI startups in India, highlighting their key focus areas and contributions in 2025. 

Here is a comprehensive overview of the 12 top AI startups in India in 2025, showcasing companies shaping the future of AI across various industries.

1. Niramai Health Analytix

Source: niramai.com 

Founded in 2016 by Dr. Geetha Manjunath and based in Bangalore, Niramai Health Analytix is an AI healthcare setup focused on early-stage breast cancer detection. The company’s core platform, Thermalytix, integrates high-resolution thermal imaging with advanced machine learning (ML) models, custom convolutional neural networks (CNNs) workflows built using Python and Tensorflow. 

• Key technology used: Thermalytix uses multi-layered machine learning algorithms combined with thermal data processing and deep neural network (DNN) to detect abnormal physiological patterns in thermal images. 
• Impact on industry: Niramai's AI pipeline automates anomaly detection with thermal imaging, eliminating reliance on radiologists, radiation, or biopsy pre-screenings and significantly reducing diagnostic delays in healthcare infrastructures.
• Notable achievements: Recognized globally by CB Insights among Top 100 AI startups. The company is awarded by Frost & Sullivan, and backed by pi Ventures, Ankur Capital, with pilots run on Dockerized microservices for deployment ease.
• Future Outlook: Niramai plans to expand into AI-assisted screening for cervical and lung cancers, focusing on real-time inference capabilities on edge computing platforms like NVIDIA Jetson Nano.

Use Case:

A public hospital in Maharashtra deployed Thermalytix, integrated with edge ML inferencing on portable devices. Over 3,000 women were screened with minimal power needs and no radiologist dependency. Diagnosis turnaround was reduced by 70%, improving access to early cancer detection in underserved regions.

2. Mad Street Den

Source: vue.ai

Founded in 2016 by Dr. Ashwini Asokan and Anand Chandrasekaran. Mad Street Den is a Chennai-based AI startup that develops computer vision and AI-powered SaaS platforms for the retail sector. Their core platform, Vue.ai, uses deep learning and neural networks deployed through APIs to automate catalog management, personalization, and visual search for global brands.

• Key technology used: Vue.ai applies convolutional neural networks (CNNs), TensorFlow frameworks, and reinforcement learning algorithms to automate product tagging, styling recommendations, and intelligent visual merchandising.
• Impact on industry: Mad Street Den reduces manual cataloging effort by over 90% through AI-driven content generation, improving product discovery and conversion rates for e-commerce and omnichannel retailers.
• Notable achievements: Backed by Sequoia Capital and Falcon Edge, the startup expanded operations across India, the US, and the Middle East, with Vue.ai deployed on AWS. 
• Future Outlook: Plans include enhancing their AI stack with synthetic data generation, self-supervised learning models, and edge-based visual engines for real-time in-store applications.

Use Case:

A major Indian fashion retailer has deployed Vue.ai to automate product cataloging. You also apply pre-trained CNN models to reduce manual tagging time by 95% and improve the inventory turnover rate by 22%

3. Arya.AI

Source: Arya.ai

Vinay Kumar and Deekshith Mangalore founded Arya.ai in 2013, and it is one of the top AI startups in India. The Mumbai-based startup is building autonomous AI platforms for the BFSI sector. The platform uses deep learning, generative AI, and self-learning systems to automate underwriting, claims assessment, and fraud detection. 

• Key technology used: Arya.ai utilizes proprietary natural language processing (NLP) frameworks, recurrent neural networks (RNNs), and AI model orchestration pipelines built using PyTorch and Kubernetes for scalable model deployment.
• Impact on industry: The platform reduces underwriting time by 40% and increases fraud detection accuracy in insurance claims by applying unsupervised anomaly detection and contextual embeddings.
• Notable achievements: Arya.ai was recognized by Nasscom as one of the Top 10 Deep Tech Startups and participated for global programs like the Paris FinTech Forum.
• Future Outlook: Arya.ai aims to extend its AI orchestration platform to decentralized finance (DeFi) compliance using federated learning and explainable AI (XAI) principles.

Use Case:

You are a part of an Indian insurance company that has implemented Arya.ai’s auto-claims assessment engine. With the help of NLP and RNN models, you reduce claim settlement time by 35% and improve customer satisfaction. 

4. SigTuple

Source: SigTuple.com

Founded in 2015 by Tathagato Rai Dastidar is one of the prominent AI startups in India, especially within the healthcare sector. SigTuple focuses on applying AI and machine learning for medical diagnostics automation. The Bangalore-based startup uses computer vision and deep neural networks to analyze visual medical data from blood smears, urine samples, and retinal scans.

• Key technology used: SigTuple’s AI platform automates diagnostic workflows using convolutional neural networks (CNNs) trained on annotated datasets, TensorFlow models, and image segmentation techniques.
• Impact on industry: SigTuple reduces dependency on manual microscopy by automating cell classification and anomaly detection, improving lab throughput and diagnostic precision in pathology labs. 
• Notable achievements: The company raised funding from IDG Ventures, Accel Partners, and pi Ventures, and its deployments run on GPU-accelerated cloud clusters for faster inference.
• Future Outlook: Plans include expanding into automated histopathology and real-time telepathology platforms using transformer-based vision models.

Use Case:

A pathologist in a pathology lab in Bangalore, deploying SigTuple’s AI-based visual analysis platform to automate hematology tests using CNN-based image classification models. The solution increased laboratory throughput by 50%, reduced manual microscopy errors by 20%, and ensured consistent, auditable reporting across decentralized lab centers. You can also integrate the platform with telepathology modules, allowing remote doctors to review flagged anomalies, reducing reporting times for critical cases by 45%. 

5. Avaamo

Source: avaamo.ai

Founded in 2014 by Ram Menon, Avaamo specializes in conversational AI and deep learning platforms for enterprise automation. Headquartered in Bangalore and California, Avaamo designs domain-specific virtual assistants that automate workflows across healthcare, telecom, and banking sectors.

• Key technology used: Avaamo's conversational AI stack combines BERT-based NLP models, knowledge graphs, and multi-turn dialogue management engines built using TensorFlow Extended (TFX).
• Impact on industry: Avaamo reduces human agent dependency by 60% in enterprise customer service operations, improving resolution speed with predictive intent recognition and contextual conversational flows.
• Notable achievements: Avaamo has raised over ₹2,490 crores, and partners with enterprises like Intel and Stanford Healthcare with solutions deployed on hybrid cloud architectures. 
• Future Outlook: Developments focus on proactive AI assistants capable of sentiment analysis, task orchestrations, and multi-modal interactions using speech and vision AI fusion. 

Use Case:

You integrate Avaamo’s BERT-powered conversational AI platform across mobile, voice, and web channels for an Indian telecom operator in Hyderabad. The system automated over 65% of tier-1 queries using TensorFlow Extended (TFX) pipelines hosted on Kubernetes clusters for scalable deployment. Moreover, you can reduce customer support response time by 45% and CSAT score by 28% over six months of operation. 

6. Observe.AI

Source: observe.ai

Founded in 2017 by Swapnil Jain, Observe.AI is ranked as one of the prominent AI startups in India. It operates in Bangalore and California and focuses on contact center transformation through AI. The platform uses deep learning models to transcribe, analyze, and optimize real-time agent-customer conversations. 

• Key technology used: Observe.AI analyzes voice interactions for call centers using hybrid ASR systems, transformer-based NLP models, and emotion detection engines.
• Impact on industry: The platform improves compliance accuracy by automating quality assurance checks and optimizing agent training programs based on AI-driven interaction scoring.
• Notable achievements: Observe.AI has secured over ₹1,660 crores from SoftBank Vision Fund and Scale Venture Partners, deploying AI workloads on AWS Lambda and Kubernetes. 
• Future Outlook: Future developments include multilingual ASR enhancements and predictive agent assist tools integrated into CRM and omnichannel contact center solutions.

Use Case:

A BFSI firm in Chennai is adopting the AI-driven call monitoring suite of Observe.AI across 5000+ agents. You can analyze voice interactions through hybrid ASR engines, boosting call compliance accuracy by 30% and reducing audit overhead by 40%. It also lets you host on Kubernetes clusters, improving first-call resolution rates by 18%. 

7. Locus.sh

Source: locus.sh

Founded in 2015 by Nishith Rastogi and Geet Garg, Locus.sh is one of the prominent AI startups in India offering intelligent supply chain decision-making. The platform uses proprietary route optimization algorithms, predictive analytics, and ML models for logistics automation.

• Key technology used: Locus.sh combines constraint-based optimization, heuristic search algorithms, and reinforcement learning for dynamic route planning and resource allocation.
• Impact on industry: The platform reduces logistics costs by 20-25% while improving on-time delivery rates through dynamic scheduling and real-time rerouting based on traffic data analytics.
• Notable achievements: Locus.sh raised over ₹1,660 crores from Tiger Global and Qualcomm Ventures. The platform expands across India, Southeast Asia, and North America, establishing it as one of the top AI startups in India. 
• Future Outlook: Future plans include enhancing AI engines with generative optimization models for last-mile logistics orchestration and predictive demand analysis.

Use Case:

You work for a leading grocery delivery company in Chennai, which deployed Locus.sh’s optimization suite across 30 tier-1 and tier-2 cities. You host dynamic routing on Kubernetes to improve vehicle utilization rates by 23% and reduce operational fleet costs by 20%. Moreover, real-time traffic prediction APIs enabled a 27% improvement in estimated delivery time accuracy. 

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8. Haptik

Source: haptik.ai

Founded in 2013 by Aakrit Vaish and Swapan Rajdev, Haptik is one of the prominent AI startups in India. It specializes in intelligent virtual assistants for enterprises. The Mumbai-based conversational AI platform blends NLP, dialog management, and knowledge mining for end-to-end customer automation.

• Key technology used: Haptik uses proprietary NLU engines, transformer-based chat frameworks, and contextual recommendation models optimized using TensorFlow Serving and Kubernetes.
• Impact on industry: Haptik reduces live agent dependency by 55% while improving customer satisfaction scores (CSAT) through AI-driven omnichannel conversational experiences.
• Notable achievements: In 2019, Reliance Jio acquired Haptik for over ₹700 crores. It now powers AI-driven conversations for leading Indian enterprises, making it one of the top AI startups in India.
• Future Outlook: Haptik is expanding autonomous agent capabilities, federated learning NLP models, and context persistence across voice, chat, and mobile platforms.

Use Case:

An e-commerce platform in Mumbai is integrating Haptik’s conversational AI to automate 70% of customer interactions. You can use TensorFlow Serving-based models to process 100000+ daily queries across WhatsApp, improving response rates by 48%. 

9. Yellow.AI

Source: yellow.ai

Yellow.ai, founded in 2016 by Raghu Ravinutala, is a Bangalore-based enterprise automation platform specializing in dynamic AI agents. It is one of the prominent AI startups in India delivering conversational AI using hybrid NLP models and deep learning across chat, voice, and email channels. Hybrid NLP combines rule-based and deep learning NLP models for better understanding.

• Key technology used: Yellow.ai uses proprietary NLP engines combined with reinforcement learning for dynamic intent flows, integrating TensorFlow Extended (TFX) and hybrid cloud Kubernetes deployments.
• Impact on industry: Their platform automates over 60% of enterprise support, HR, and IT tasks, reducing operational costs and enhancing employee or customer experience with proactive AI-driven workflows.
• Notable achievements: The company raised ₹6,640 crores from investors like WestBridge Capital and Salesforce Ventures; expanded to over 30 countries with ISO 27001-certified infrastructure.
• Future Outlook: Yellow.ai is building emotion-aware conversational systems using multimodal AI and expanding self-service platforms. The platform integrates with enterprise ERPs and CRMs, making it one of the top AI startups in India. 

Use Case:

A major BFSI group in India is implementing Yellow.ai’s dynamic voice and chatbots to automate customer onboarding. You use NLP pipelines to process KYC validations and customer queries in real-time, reducing onboarding time by 50%. You can also deploy the platform on Azure Kubernetes clusters, enabling secure scaling across multiple languages and improving customer engagement by 34%. 

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10. Uniphore

Source: uniphore.com

Founded in 2008 by Umesh Sachdev and Ravi Saraogi, Uniphore is a Chennai-headquartered AI startup that focuses on conversational automation for enterprises. It is one of the popular AI startups in India that integrates speech recognition, computer vision, and emotion detection models across customer interactions.

• Key technology used: Uniphore uses deep neural networks (DNNs), speech-to-text (STT) engines, and emotion AI models optimized on NVIDIA A100 GPUs for real-time interaction analytics.
• Impact on industry: The platform automates call summarization, sentiment scoring, and compliance tracking, reducing manual documentation time by 70% in customer service operations.
• Notable achievements: Uniphore has secured over ₹10,370 crores in funding from March Capital and Cisco Investments. Moreover, the company is expanding operations across India, APAC, and North America, substantiating it as one of the top AI startups in India.
• Future Outlook: Uniphore plans to build AI-led virtual coaching platforms for sales agents and real-time predictive analysis using multimodal data fusion techniques.

Use Case:

A leading telecom provider in Chennai is struggling with poor data analytics. You deploy Uniphore’s emotion AI analytics across its contact centers. You can gather data using real-time emotion scoring models, flag customer frustration, enable live escalation, and increase first-call resolution rates by 22%. 

11. Rephrase.AI

Source: rephrase.ai

Rephrase.ai, founded in 2019 by Ashray Malhotra and Shivam Mangla, is a Bangalore-based AI startup specializing in synthetic media generation. Their platform uses generative adversarial networks (GANs) and deep learning to create large-scale hyper-realistic personalized videos. Rephrase.ai fine-tunes avatar lip movement to match multilingual speech inputs for better personalization. 

• Key technology used: Rephrase.ai employs GAN-based video synthesis models trained on high-fidelity datasets, leveraging PyTorch and proprietary model compression techniques for scalable video generation.
• Impact on industry: The platform enables brands to automate personalized marketing campaigns, reducing content production costs by 40% and increasing engagement through hyper-customized videos.
• Notable achievements: Secured ₹415 crores from Lightspeed India Partners; partnered with major Indian brands across fintech, education, and consumer goods sectors.
• Future Outlook: Plans include advancing lip-sync quality for multilingual video outputs and integrating real-time video personalization APIs for e-commerce platforms.

Use Case:

An Indian edtech platform integrated Rephrase.ai’s personalized video generation engine for student engagement campaigns. Within days, you used GAN-based models to produce custom onboarding videos for 50,000+ users in regional languages. The campaign engagement rates improved by 47%, with a 25% faster onboarding-to-purchase conversion rate.

12. Qure.AI

Source: qure.ai

Founded in 2016 by Prashant Warier and Pooja Rao, Qure.ai is a Mumbai-based healthtech startup specializing in deep learning-based medical imaging analysis. Their AI solutions automate the interpretation of X-rays, CT scans, and MRIs to assist radiologists.

• Key technology used: Qure.ai’s platform uses convolutional neural networks (CNNs), segmentation algorithms, and TensorFlow models deployed on GPU-backed cloud environments for real-time diagnostic support.
• Impact on industry: Their AI systems reduce radiologist workload by automating triage, improving diagnostic accuracy in detecting TB, brain trauma, and COVID-19 anomalies.
• Notable achievements: The company raised ₹1,245 crores from Sequoia India and MassMutual Ventures, and WHO-recognized solutions were adopted on the platform in over 40 countries.
• Future Outlook: Expansion plans include AI models for predictive disease progression and mobile-based lung disease screening kits powered by edge inference.

Use Case:

You are working in the Mumbai municipal hospital, deploying Qure.ai’s qXR platform for mass tuberculosis screening. The AI models processed over 10,000 chest X-rays in two weeks, reducing manual screening time by 60%. In addition, you could automatically flag high-risk cases, improving patient triage speed and referral efficiency across public health facilities. 

13. Embibe

Source: embibe.com

Embibe, founded in 2012 by Aditi Avasthi, is a Bangalore-based AI-driven education platform focused on personalized learning and performance analytics. It utilizes data science and AI to enhance student engagement and academic outcomes. The platform uses predictive analytics to anticipate learning gaps, offering actionable insights for both students and educators. 

• Key technology used: Embibe uses knowledge graphs, Bayesian inference models, and neural networks to generate adaptive learning content and predictive academic assessments.
• Impact on industry: Their AI-driven platform improves learning outcomes by personalizing content recommendations and predicting exam readiness with 92% accuracy based on engagement metrics.
• Notable achievements: Acquired by Reliance Industries for ₹6,640 crores and adopted across India's CBSE, JEE, and NEET coaching networks.
• Future Outlook: Embibe is working toward developing real-time AI-driven tutoring capabilities powered by natural language generation (NLG) and multimodal content synthesis. 

Use Case:

A large Delhi-based coaching chain integrated Embibe’s AI assessment engines for JEE aspirants. You use knowledge graph-based predictions personalized practice schedules for 20,000+ students, improving syllabus coverage completion by 30%. The predictive performance analysis allowed targeted intervention, improving final test scores across batches by 18%.

14. Wysa

Source: wysa.com

Founded in 2016 by Jo Aggarwal and Ramakant Vempati, Wysa is a Bangalore-based AI-powered mental health platform. It provides AI-based chatbots using evidence-based therapeutic techniques like CBT (Cognitive Behavioral Therapy) and mindfulness exercises.

• Key technology used: Wysa uses transformer-based dialogue systems, emotion recognition models, and anonymized data analytics to deliver clinically validated mental health support at scale.
• Impact on industry: The platform democratizes mental health care by offering 24/7 support with 92% conversational efficacy, reducing therapy gaps in urban and rural populations.
• Notable achievements: The startup raised ₹415 crores from W Health Ventures and pi Ventures. Moreover, it has been approved by the NHS (UK) and is listed as one of the top digital mental health solutions globally.
• Future Outlook: Future updates include personalized therapy tracks and therapist handoff systems based on risk-stratification AI models. Personalized therapy tracks and therapist handoff systems based on risk-stratification AI models are underpinned by ML and predictive analytics technologies.

Use Case:

An Indian university deployed Wysa’s mental health chatbot across its 10,000+ student community. You use Wysa’s Emotion analytics engines to flag stress patterns during exam seasons, providing early interventions and personalized self-help programs. The student self-reported stress levels dropped by 22% over two academic terms, validating the platform's preventive mental health capabilities.

Also read: Leading AI Companies Driving Innovation Across Industries

Now, let’s examine some of the factors that help AI startups stand out in the global market.

What Makes AI Startups in India Stand Out in the Global Market?

AI startups in India use innovative technologies like deep learning, ML, and NLP to build scalable AI-driven solutions. These startups also utilize predictive analytics and data-driven insights for sectors like agriculture, healthcare, and financial institutions. Moreover, state-of-the-art frameworks like PyTorch and Keras enable rapid prototyping and scalable AI deployment strategies for the global market. 

Here are some of the factors that make AI startups in India stand out from global competition:

• Focus on local problems with global solutions: AI startups in India develop solutions using AI, IoT architectures, and big data to address local challenges like healthcare accessibility, rural education, and agricultural automation.
• Rapid investment growth: In recent years, venture capital from firms like Sequoia India, Accel, and SoftBank Vision Fund has poured into Indian AI ventures.
• Using India’s data infrastructure for scalable AI Solutions: Startups are tapping into India's massive mobile internet user base and government-supported data initiatives such as Aadhaar and DigiLocker to create AI models. This accessible data infrastructure allows the development of ML models, predictive analytics, and personalized AI solutions.
• Integration of Edge Computing: Indian AI startups are adopting edge computing to improve real-time data processing and latency reduction, for sectors like smart agriculture, IoT devices, and autonomous vehicles. To build cost-effective solutions, startups use edge AI frameworks and platforms like NVIDIA Jetson and Google Coral. 
• Government initiatives and support: The Indian government’s initiatives, such as Digital India and Atal Innovation Mission, and IndiaAI mission provide grants, infrastructure, and policy support that help accelerate AI adoption.

Also read: Artificial Intelligence Jobs in 2025: Skills and Opportunities

Now, let’s look at some of the major challenges for AI startups in India. 

What Challenges Do AI Startups Face in India?

AI startups in India face bottlenecks in securing funding, especially with the high competition from global and domestic tech giants. There is also a severe knowledge gap for AI and data science talents in tier-1 and tier-2 cities. Moreover, scaling enterprise-grade AI solutions to meet global and local demands is another challenge in the context of data privacy and AI model deployment. 

Here are some of the challenges that AI startups face in 2025:

• Difficulty securing funding: Despite growing investor interest, several AI startups struggle to secure early-stage funding due to uncertain monetization strategies and long-term ROI. 
• Shortage of skilled employees: India faces a severe AI talent shortage, and you may notice a high demand for data scientists skilled in NLP, deep learning, and reinforcement learning. Larger firms often poach top-tier talent, leaving startups with a limited pool of qualified professionals. 
• Data privacy regulations: The Digital Personal Data Protection (DPDP) Act of 2023 requires startups to implement complex data security measures, manage user content, and ensure compliance. The high cost of maintaining compliance, coupled with the potential of heavy fines, makes it difficult for startups, especially with differing privacy laws. 
• Challenges in scaling AI solutions: To scale AI solutions, you must overcome challenges in cloud-native architecture deployment, edge computing, and more. Most startups across India utilize Kubernetes, Docker, and CI/CD pipelines to handle scaling across multiple geographies and high-traffic environments. 
• Operationalizing AI models for real-time predictions: Deploying AI models in real-time environments requires efficient model inference, low-latency architecture, and continuous integration of new data. To overcome this, AI startups often use Apache Kafka and TensorFlow Serving for real-time updates and low-latency processing.
• High-cost infrastructure: Many AI startups struggle with high computational costs and training deep learning models. Cloud services such as AWS SageMaker, Google AI Platform, and Azure ML can help with distributed training and inference. However, managing cost-effective resources and GPU access remains a major bottleneck for AI startups. 

Example Scenario:

A manufacturing AI startup in Bangalore faces difficulties securing Series A funding due to uncertain ROI. TensorFlow for predictive maintenance can be a solution for cloud infrastructures like AWS; however, scaling across multiple regions is a problem. Data privacy remained a significant issue for global expansion efforts despite using Docker containers for deployments. 

Also read: Scope of Artificial Intelligence in Different Industries Explained

Advance Your AI Career with upGrad’s Industry-Ready Programs!

In 2025, AI startups in India are leading the charge in transforming industries with technologies such as deep learning, NLP, and edge computing. Despite challenges like funding constraints and talent acquisitions, startups are developing innovative solutions like chatbots to meet local and global demands. 

If you want to stay ahead of your peers with industry-relevant AI skills, look at upGrad’s courses that allow you to be future-ready. These are some of the additional courses that can help you gain access to an AI startup. 

• Generative AI Mastery Certificate for Software Development
• Advanced Prompt Engineering Course with ChatGPT
• Artificial Intelligence in the Real World

Curious which courses can help you get an opportunity in top AI startups in India? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center. 

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References 

https://pib.gov.in/PressReleasePage.aspx?PRID=2108810#:~:text=The%20country%20is%20home%20to,with%20a%20CAGR%20of%2045%25.