Gen AI Training in Poruris designed to provide in-depth knowledge of Generative AI systems, Large Language Models, and modern AI application development. Gen AI Course in Porurfocuses on Prompt Engineering, LLM workflows, and enterprise AI solutions. Gen AI Certification Training in Porurhelps learners gain strong skills in AI model usage and intelligent application development. The training covers real-time Gen AI use cases used in modern software and enterprise environments. It is suitable for beginners as well as experienced IT professionals. Learners gain practical exposure through hands-on labs and real-time AI projects. Flexible learning options such as online and classroom sessions are available. Expert trainers provide strong conceptual and practical guidance. This training improves career readiness in Gen AI and AI engineering roles.

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Upcoming Future Transformations in Gen AI Training in Porur

• Rapid Evolution of Large Language Models (LLMs): The future of Gen AI Training in Porur is strongly influenced by the continuous evolution of Large Language Models that are becoming the foundation of modern artificial intelligence applications. LLMs are transforming the way organizations develop intelligent systems by enabling machines to understand natural language, generate meaningful content, analyze complex information, and provide human-like responses. Organizations are increasingly adopting LLM-based solutions for AI assistants, automated content creation, enterprise search systems, customer support applications, and business intelligence platforms. Future Gen AI Training programs will focus on advanced LLM concepts including transformer architecture, model training techniques, token processing, context management, model optimization, and real-time implementation strategies. Professionals with strong knowledge of LLM workflows and AI application development will have excellent opportunities in the rapidly growing Generative AI industry.
• Expansion of Generative AI Across Multiple Business Domains: Generative AI is expanding beyond traditional technology sectors and becoming an important solution across healthcare, finance, banking, education, manufacturing, retail, and enterprise industries. Organizations are adopting AI-powered solutions to automate business operations, improve customer experiences, analyze large volumes of data, and enhance decision-making processes. Gen AI Training in Porur helps learners understand how Generative AI technologies are applied in different industries through real-world use cases and practical implementations. Future AI professionals will require knowledge of AI models, automation frameworks, enterprise integration methods, and industry-specific AI solutions to build innovative applications that solve complex business challenges.
• Growth of AI Agents and Autonomous Intelligent Systems: AI Agents are becoming one of the fastest-growing areas in Generative AI by enabling systems to perform tasks independently using AI models, external tools, APIs, and automated workflows. Organizations are moving towards autonomous AI systems that can analyze information, make decisions, complete business tasks, and improve productivity without continuous human involvement. Future Gen AI Training focuses on AI Agent architecture, intelligent workflow automation, agent communication, tool integration, and building advanced AI applications using frameworks such as LangGraph and LangChain. AI Agents will play a major role in enterprise automation, virtual assistants, research systems, and intelligent business process management.
• Advanced Development of Prompt Engineering Techniques: Prompt Engineering is becoming a critical skill for professionals working with Generative AI technologies. Effective prompting helps users improve AI model accuracy, control generated responses, and achieve better results from Large Language Models. Future Gen AI Training programs will focus on advanced prompt design techniques, structured instructions, context optimization, role-based prompting, and practical prompt engineering strategies used in enterprise applications. Professionals will learn how to create efficient prompts for content generation, data analysis, automation tasks, customer interactions, and AI-powered software solutions. Prompt Engineering will continue to remain an essential skill for maximizing the capabilities of Generative AI systems.
• Increasing Adoption of Retrieval Augmented Generation (RAG) Systems: Retrieval Augmented Generation is transforming the way organizations build reliable and knowledge-based AI applications. RAG combines Large Language Models with external data sources, allowing AI systems to generate accurate responses based on company-specific information. Future Gen AI Training focuses on RAG architecture, document processing, embedding generation, vector database management, and knowledge retrieval techniques. Organizations are adopting RAG solutions for enterprise search, customer support systems, document analysis, and intelligent information platforms. Professionals with expertise in RAG implementation will play a key role in developing accurate, scalable, and business-focused AI applications.
• Integration of Generative AI with Enterprise Applications and Cloud Platforms: The integration of Generative AI with enterprise applications is becoming a major transformation in modern businesses. Organizations are connecting AI models with cloud platforms, databases, APIs, software applications, and business systems to improve automation and productivity. Future Gen AI Training includes AI application integration concepts, API connectivity, cloud-based AI deployment, enterprise workflow automation, and scalable AI solution development. This integration enables companies to build intelligent applications that support decision-making, improve operational efficiency, and create personalized user experiences across different business environments.
• Advanced AI Model Fine-tuning and Domain-Specific AI Solutions: Fine-tuning is becoming an important approach for organizations that require customized AI models for specific business requirements. Pre-trained AI models can be modified and optimized to perform specialized tasks in areas such as healthcare analysis, financial forecasting, legal documentation, customer service, and technical support. Future Gen AI Training focuses on model customization, fine-tuning techniques, performance evaluation, data preparation, and optimization methods. Professionals will gain knowledge of developing domain-specific AI solutions that deliver improved accuracy, better performance, and enhanced business value.
• Future Development of Multimodal AI Systems: Multimodal AI represents the next generation of Generative AI by enabling systems to understand and process multiple forms of information including text, images, audio, and video. Organizations are adopting multimodal AI applications for intelligent assistants, image understanding, automated content creation, advanced search systems, and interactive user experiences. Gen AI Training provides knowledge of multimodal AI architecture, data processing methods, model integration techniques, and real-time application development. Multimodal AI will continue to expand the capabilities of intelligent systems and create new opportunities in AI innovation.
• Growth of Gen AI Tools, Frameworks, and Development Platforms: The Generative AI ecosystem is rapidly growing with advanced tools and frameworks that simplify AI application development. Technologies such as OpenAI GPT Models, LangChain, Hugging Face Transformers, Vector Databases, LangGraph, and AI development platforms are becoming essential skills for AI professionals. Future Gen AI Training programs focus on practical implementation of these tools, helping learners develop AI-powered applications, automate workflows, manage AI models, and build enterprise-level solutions. Knowledge of modern Gen AI tools will help professionals stay competitive in the evolving AI technology landscape.
• Continuous Innovation, AI Ethics, and Career Opportunities in Gen AI: Generative AI is continuously evolving with new models, frameworks, automation techniques, and intelligent application possibilities. Organizations are focusing on responsible AI development, data privacy, security, and ethical AI implementation while adopting advanced AI solutions. Future Gen AI professionals must continuously upgrade their skills in LLMs, AI Agents, Prompt Engineering, RAG systems, and AI application development. Gen AI Training in Porur helps learners gain practical expertise, complete real-time projects, and prepare for career opportunities such as Generative AI Engineer, AI Developer, Machine Learning Engineer, Prompt Engineer, and AI Solution Architect roles.

Building Tools and Techniques with Gen AI Training in Porur

• OpenAI GPT Models: OpenAI GPT Models are advanced Generative AI models designed to understand natural language, generate human-like responses, and perform complex reasoning tasks. These models are widely used for developing AI-powered applications such as chatbots, virtual assistants, content generation platforms, coding assistants, document analysis systems, and enterprise automation solutions. Gen AI Training in Porur helps learners understand GPT model architecture, API integration, prompt-based interactions, and real-time implementation approaches. Learners gain practical knowledge of using GPT models for text generation, summarization, translation, question answering, and business process automation. These models support organizations in improving productivity by automating repetitive tasks and enhancing decision-making capabilities. GPT models can be integrated with enterprise applications, databases, cloud platforms, and AI workflows to build scalable intelligent solutions. They are considered one of the most important technologies driving modern Generative AI adoption across industries such as IT, healthcare, finance, education, and business services.
• Large Language Models (LLM): Large Language Models (LLMs) are powerful Artificial Intelligence models trained on massive volumes of text data to understand language patterns and generate meaningful responses. They are the core foundation behind Generative AI applications including conversational AI, search assistants, recommendation systems, and intelligent automation platforms. Gen AI Course in Porur provides detailed knowledge of LLM architecture, transformer models, token processing, model training concepts, and real-world application development. Learners understand how LLMs analyze context, predict responses, and generate accurate outputs based on user instructions. LLMs support various Natural Language Processing tasks such as text classification, summarization, translation, sentiment analysis, and content creation. They can be customized and integrated with enterprise systems to solve business challenges through AI-powered solutions. Understanding LLM technology helps professionals develop advanced AI applications and build expertise in modern AI engineering roles.
• Prompt Engineering: Prompt Engineering is a critical skill in Generative AI that focuses on designing effective instructions to achieve accurate, relevant, and optimized AI-generated responses. It involves creating structured prompts, managing context, defining AI roles, and improving interaction quality with Large Language Models. Gen AI Certification Training in Porur teaches advanced prompting techniques including zero-shot prompting, few-shot prompting, chain-of-thought prompting, and role-based prompting strategies. Learners gain practical experience in creating prompts for content generation, automation workflows, data analysis, and enterprise AI applications. Prompt Engineering helps professionals control AI output quality, reduce incorrect responses, and improve overall model performance. It is widely used in AI assistants, customer support automation, business intelligence tools, and intelligent application development. Strong prompt engineering skills enable professionals to maximize the capabilities of Generative AI systems.
• LangChain: LangChain is a popular framework used for building powerful applications using Large Language Models and Generative AI technologies. It provides tools and components required to connect AI models with external data sources, APIs, databases, documents, and business applications. Gen AI Training in Porur covers LangChain architecture, chains, agents, memory management, prompt templates, and retrieval-based application development. Learners understand how LangChain helps create AI-powered assistants, document processing systems, question-answering applications, and enterprise automation solutions. It enables developers to combine multiple AI capabilities into structured workflows for complex application requirements. LangChain supports integration with different LLM providers and vector databases to create scalable AI solutions. It is widely adopted by developers and organizations building next-generation Generative AI applications.
• Hugging Face Transformers: Hugging Face Transformers is a widely used open-source platform that provides thousands of pre-trained Artificial Intelligence models for Natural Language Processing, Machine Learning, and Generative AI development. It supports transformer-based models for text generation, language understanding, translation, summarization, classification, and conversational AI applications. Gen AI Training in Porur helps learners understand transformer architecture, model selection, deployment techniques, and customization approaches. Learners gain practical exposure to working with pre-trained models and adapting them for different business requirements. Hugging Face provides powerful libraries and tools that simplify AI model development and experimentation. It supports researchers, developers, and organizations in building advanced AI applications using open-source technologies. Hugging Face Transformers plays an important role in accelerating Generative AI innovation and enterprise AI adoption.
• Vector Databases: Vector Databases are specialized data storage systems designed to store and retrieve high-dimensional vector representations generated by AI models. They are essential for Generative AI applications that require semantic search, recommendation systems, and knowledge retrieval capabilities. Gen AI Course in Porur explains vector embeddings, indexing methods, similarity search techniques, and database integration concepts. Learners understand how vector databases improve AI application accuracy by retrieving relevant information based on meaning rather than simple keyword matching. They are widely used in Retrieval Augmented Generation (RAG) systems, AI assistants, enterprise search platforms, and recommendation engines. Vector databases help organizations manage large amounts of unstructured data and make it accessible for AI-powered applications. They are a key technology for developing intelligent and context-aware Generative AI solutions.
• RAG (Retrieval Augmented Generation): Retrieval Augmented Generation (RAG) is an advanced Generative AI technique that combines Large Language Models with external knowledge sources to generate accurate and context-aware responses. RAG improves AI performance by retrieving relevant information from documents, databases, and enterprise knowledge repositories before generating answers. Gen AI Certification Training in Porur provides practical knowledge of RAG architecture, retrieval workflows, embedding generation, and vector database integration. Learners understand how RAG reduces hallucination problems and improves reliability in AI-generated responses. RAG is widely used in enterprise chatbots, customer support systems, knowledge management platforms, and document intelligence applications. Organizations use RAG solutions to connect AI models with internal business information while maintaining accuracy and security. It has become one of the most important approaches for developing enterprise-ready Generative AI applications.

Roles and Responsibilities of Gen AI Training in Porur

• Gen AI Engineer: A Gen AI Engineer designs, develops, and implements Generative AI solutions using advanced technologies such as Large Language Models, OpenAI GPT Models, AI Agents, and Retrieval Augmented Generation systems. They are responsible for building intelligent applications that can generate content, analyze information, automate workflows, and support business decision-making. They work on integrating AI models with enterprise applications, APIs, databases, and cloud platforms. Gen AI Engineers optimize prompts, evaluate model performance, and improve AI response accuracy based on business requirements. They collaborate with data scientists, software developers, and business teams to create scalable AI solutions. They also research emerging AI technologies, experiment with new frameworks, and implement best practices for developing reliable and efficient Generative AI applications.
• AI Application Developer: AI Application Developers are responsible for designing and developing software applications powered by Generative AI technologies. They integrate Large Language Models, APIs, AI frameworks, and automation tools to build intelligent applications such as chatbots, virtual assistants, recommendation systems, and enterprise AI platforms. They work with technologies including LangChain, Hugging Face Transformers, Vector Databases, and RAG frameworks to create AI-powered solutions. They develop application workflows, manage AI model communication, and ensure smooth integration between AI components and business systems. AI Application Developers also perform debugging, testing, optimization, and deployment activities to deliver high-quality AI applications. They collaborate with cross-functional teams to transform business requirements into practical AI solutions.
• Prompt Engineer: Prompt Engineers specialize in designing, testing, and optimizing prompts to improve the performance of Generative AI models. They create effective instructions for Large Language Models and GPT-based systems to generate accurate, meaningful, and context-aware responses. They analyze AI outputs, identify improvement areas, and develop advanced prompting strategies including zero-shot prompting, few-shot prompting, role-based prompting, and contextual prompting. Prompt Engineers work closely with AI developers and business teams to create efficient AI workflows for automation, content generation, data analysis, and customer interaction systems. They continuously evaluate model behavior and refine prompts to improve accuracy, consistency, and reliability of AI applications.
• Machine Learning Engineer: Machine Learning Engineers develop, train, optimize, and deploy machine learning models that support Generative AI applications. They work on data preparation, model evaluation, algorithm selection, and performance improvement activities. They handle AI model pipelines, experiment with different architectures, and implement machine learning techniques to improve application intelligence. They work with transformer models, neural networks, embeddings, and fine-tuning approaches to customize AI systems. Machine Learning Engineers collaborate with data scientists and Gen AI developers to create efficient AI solutions. They ensure AI models are scalable, accurate, and optimized for production environments.
• AI Solutions Architect: AI Solutions Architects design complete Generative AI architectures and define strategies for implementing enterprise AI solutions. They analyze business requirements and select suitable AI models, frameworks, infrastructure, and deployment approaches. They design AI systems using Large Language Models, RAG architecture, Vector Databases, cloud platforms, and AI automation frameworks. They ensure AI applications are secure, scalable, reliable, and aligned with organizational objectives. AI Solutions Architects guide development teams, review technical designs, and establish best practices for AI implementation. They play an important role in transforming business challenges into innovative AI-driven solutions.
• Data Scientist: Data Scientists analyze large datasets and develop data-driven solutions that support Generative AI applications. They perform data collection, cleaning, preparation, analysis, and visualization to create high-quality datasets for AI models. They work on feature engineering, statistical analysis, machine learning algorithms, and model evaluation techniques. Data Scientists improve AI system performance by analyzing model outputs and identifying opportunities for optimization. They collaborate with Gen AI Engineers to prepare training data, evaluate AI models, and enhance application intelligence. Their role is essential for building accurate, reliable, and efficient AI systems.
• RAG Developer: RAG Developers specialize in building Retrieval Augmented Generation applications that combine Large Language Models with external knowledge sources. They design retrieval pipelines using embeddings, Vector Databases, and document processing techniques. They develop AI systems that can access enterprise documents, databases, and knowledge repositories to generate accurate responses. RAG Developers optimize retrieval performance, improve context management, and reduce hallucination issues in AI outputs. They work with frameworks such as LangChain and AI APIs to build intelligent search systems, enterprise assistants, and knowledge-based applications. They ensure AI applications provide reliable and relevant information to users.
• AI Model Specialist: AI Model Specialists focus on selecting, customizing, evaluating, and maintaining Generative AI models for specific business requirements. They analyze different AI models, compare performance, and identify the best solutions for various applications. They work on fine-tuning models, improving accuracy, managing model parameters, and optimizing AI performance. They perform testing and validation to ensure AI models produce reliable results. AI Model Specialists collaborate with engineering teams to integrate AI models into production environments. They continuously monitor AI advancements and implement new technologies to improve enterprise AI capabilities.
• AI Product Manager: AI Product Managers manage the planning, development, and delivery of AI-powered products and solutions. They identify business opportunities where Generative AI can create value and define product strategies accordingly. They work with AI engineers, developers, designers, and stakeholders to develop AI features and functionalities. They manage product roadmaps, prioritize requirements, track development progress, and ensure successful implementation. AI Product Managers evaluate user feedback, analyze product performance, and continuously improve AI solutions. They ensure AI products meet customer expectations, business goals, and technology standards.
• AI Quality Analyst: AI Quality Analysts are responsible for testing and validating Generative AI applications to ensure accuracy, reliability, and performance. They create test scenarios for AI models, prompts, workflows, and application functionality. They evaluate AI responses, identify incorrect outputs, and analyze model behavior under different conditions. They work with developers to resolve issues and improve AI application quality. AI Quality Analysts ensure AI systems follow performance standards, security requirements, and business expectations. Their role helps organizations deliver reliable and user-friendly AI solutions.
• AI Project Manager: AI Project Managers oversee the planning, execution, and delivery of Generative AI projects. They coordinate between AI engineers, developers, data scientists, business teams, and stakeholders to ensure successful project completion. They manage project timelines, resources, budgets, risks, and communication processes. They monitor AI development activities, track milestones, and ensure solutions meet business requirements. AI Project Managers help teams adopt effective project methodologies and maintain smooth collaboration. They play a key role in delivering enterprise AI projects successfully.
• AI Research Scientist: AI Research Scientists explore new Generative AI technologies, develop advanced AI methodologies, and improve existing AI models. They research Large Language Models, transformer architectures, multimodal AI systems, and emerging AI frameworks. They conduct experiments, analyze research findings, and develop innovative approaches for improving AI capabilities. AI Research Scientists contribute to model optimization, fine-tuning techniques, and advanced AI applications. They collaborate with engineering teams to convert research concepts into practical AI solutions. Their work drives innovation and future development in Generative AI technologies.
• AI Automation Specialist: AI Automation Specialists design automated workflows using Generative AI technologies to improve business efficiency. They integrate AI models with enterprise applications, APIs, and automation platforms to reduce manual effort. They develop intelligent workflows for document processing, customer support, data analysis, and business operations. They work with AI Agents, LangChain workflows, and automation frameworks to create smart solutions. AI Automation Specialists analyze business processes and identify opportunities where AI can improve productivity. They help organizations adopt AI-driven automation strategies.

Top Companies Hiring for Gen AI Professionals

• Deloitte: Deloitte hires Gen AI professionals to design, develop, and implement enterprise-level Generative AI solutions. Professionals work on Large Language Models (LLM), OpenAI GPT Models, Prompt Engineering, and AI application development. They handle AI automation projects, RAG-based systems, vector database integration, and intelligent workflow solutions. Deloitte provides opportunities to work on enterprise AI transformation projects across industries such as finance, healthcare, and technology. Employees gain exposure to LangChain, AI Agents, embeddings, and multimodal AI applications. Professionals contribute to building scalable AI systems, improving business processes, and implementing responsible AI practices. Deloitte offers global project exposure, advanced AI learning opportunities, and strong career growth for Gen AI engineers and consultants.
• Ernst & Young (EY): EY recruits Gen AI professionals for AI consulting, enterprise automation, and intelligent business transformation projects. Professionals work with Large Language Models (LLM), Generative AI frameworks, Prompt Engineering techniques, and AI-driven application development. EY provides opportunities to develop RAG systems, AI agents, vector search solutions, and AI-powered enterprise platforms. Employees contribute to AI strategy development, model evaluation, and responsible AI implementation. They work on integrating Gen AI technologies with business applications to improve productivity and decision-making. EY supports continuous learning through advanced AI projects, certifications, and professional development programs. It provides strong opportunities for building careers in AI consulting, AI engineering, and digital transformation roles.
• KPMG: KPMG employs Gen AI professionals for developing intelligent automation solutions and enterprise AI applications. Professionals work with OpenAI GPT Models, Large Language Models, Prompt Engineering, and AI workflow automation. They design AI solutions using LangChain, Vector Databases, RAG architecture, and AI Agent frameworks. KPMG professionals support organizations in implementing secure and scalable Generative AI systems. Employees work on AI governance, model performance optimization, and enterprise AI adoption strategies. They participate in AI transformation projects across multiple industries including banking, healthcare, and financial services. KPMG provides opportunities to enhance technical expertise, consulting skills, and advanced Generative AI capabilities.
• HCL Technologies: HCL Technologies hires Gen AI professionals for AI application development, automation, and enterprise AI engineering roles. Professionals work on Large Language Models (LLM), OpenAI GPT Models, Hugging Face Transformers, and AI-based application solutions. They develop AI-powered systems using Prompt Engineering, RAG pipelines, embeddings, and vector databases. HCL provides exposure to AI Agents, LangChain workflows, and multimodal AI technologies. Employees contribute to building scalable AI solutions for global clients and enterprise environments. They work on AI model integration, optimization, deployment, and performance monitoring. HCL offers strong career opportunities in Generative AI development, AI engineering, and enterprise automation projects.
• Capita: Capita recruits Gen AI professionals for intelligent automation, AI workflow development, and enterprise AI implementation projects. Professionals work with Generative AI models, Large Language Models, Prompt Engineering, and AI-based business solutions. They develop AI applications using RAG systems, embeddings, vector databases, and LangChain frameworks. Employees focus on improving AI accuracy, automation efficiency, and business process optimization. Capita provides opportunities to work on AI-powered customer solutions and enterprise digital transformation initiatives. Professionals gain practical experience in AI model integration, monitoring, and optimization. The organization supports career growth in AI engineering, automation, and Generative AI technology domains.
• Tech Mahindra: Tech Mahindra hires Gen AI professionals for developing advanced AI applications, automation platforms, and enterprise AI solutions. Professionals work with OpenAI GPT Models, Large Language Models, AI Agents, and Prompt Engineering techniques. They design AI workflows using LangChain, LangGraph, RAG systems, and vector database technologies. Employees support AI-driven digital transformation projects for global organizations. They work on AI model customization, fine-tuning, and intelligent application development. Tech Mahindra provides exposure to real-time Gen AI projects across industries such as telecom, healthcare, and finance. It offers excellent career opportunities for AI engineers, developers, and Generative AI specialists.
• Hitachi Vantara: Hitachi Vantara recruits Gen AI professionals for enterprise AI infrastructure, intelligent data solutions, and AI application development roles. Professionals work with Large Language Models, embeddings, vector databases, and AI-powered analytics platforms. They support Gen AI implementation using RAG architecture, AI Agents, and advanced AI workflows. Employees focus on improving AI system performance, scalability, and data-driven decision-making. Hitachi provides opportunities to work on enterprise AI platforms and cloud-based AI solutions. Professionals gain experience in AI optimization, model integration, and intelligent automation. The organization supports global career growth in Generative AI engineering and AI innovation projects.
• NTT Data: NTT Data hires Gen AI professionals for enterprise AI development, automation, and intelligent application solutions. Professionals work with OpenAI GPT Models, Large Language Models, Prompt Engineering, and AI workflow automation. They develop AI applications using LangChain, LangGraph, RAG systems, and vector databases. Employees support organizations in adopting Generative AI technologies for improving business operations. They work on AI model deployment, integration, testing, and optimization activities. NTT Data provides exposure to global AI transformation projects and advanced AI technologies. It offers strong career opportunities in AI development, AI consulting, and enterprise Generative AI solutions.
• DXC Technology: DXC Technology recruits Gen AI professionals for AI modernization, automation, and enterprise intelligent solutions. Professionals work with Generative AI models, LLM applications, OpenAI GPT Models, and AI development frameworks. They implement Prompt Engineering, RAG architectures, AI Agents, and vector-based search solutions. Employees support AI-driven transformation projects for global customers. They focus on AI system integration, model optimization, and scalable AI deployment. DXC provides opportunities to work with advanced AI technologies and enterprise cloud platforms. The organization supports professional growth in Generative AI engineering, AI automation, and intelligent application development.
• Oracle: Oracle hires Gen AI professionals for cloud-based AI solutions, enterprise AI integration, and intelligent application development. Professionals work with Large Language Models, OpenAI GPT Models, Prompt Engineering, and AI automation technologies. They develop AI solutions using RAG systems, vector databases, embeddings, and AI workflow frameworks. Employees contribute to Oracle Cloud AI initiatives and enterprise digital transformation projects. They work on AI model deployment, optimization, and integration with business applications. Oracle provides exposure to advanced cloud AI technologies and large-scale enterprise environments. It offers excellent career opportunities in Generative AI development, AI engineering, and cloud AI solutions.