Identify and Transform AI Workloads with CloudAtlas using Generative AI
CloudAtlas evaluates existing and potential AI applications and workloads to identify opportunities to leverage generative AI processes and services in the cloud and to formulate a generative AI strategy to drive innovation.
Make the Most of AI Services in the Cloud with CloudAtlas
Are you looking to transform workloads to the cloud to take advantage of cloud AI capabilities? With a generative AI data readiness assessment, CloudAtlas simplifies and speeds the process by identifying where and how workloads can leverage AI. CloudAtlas can also identify how chipsets and AI accelerators can be used to enable you to use AI more efficiently and cost effectively, whether in the public or private cloud. CloudAtlas can then recommend how these workloads can be optimized, identifies the best AI chipset and hardware configuration to best leverage AI, and helps better utilize existing or incorporate new cloud, generative AI and/or Machine Learning services to develop new applications and databases to automate manual tasks, improve decision-making, provide personalized experiences, and more.
Just a Few of the Many Cloud AI Use Cases Enabled by CloudAtlas :
Automated Customer Support
Enhance customer satisfaction with AI-powered virtual assistants streamline customer support processes, handle routine inquiries, and speed resolution.
Text Analytics with Natural Language Processing (NLP)
AI algorithms based on NLP processes text data to understand context, identify key entities, and extract sentiment to tailor support actions.
Voice and Speech Analysis
Analyze the tone and language from voice interactions to determine customer satisfaction to craft customized responses to improve the customer experience.
Predictive Analytics
Leverage AI algorithms to analyze historical sales data, market trends, and more to predict product demand to improve inventory management, inform sales campaigns, and improve overall operational efficiency.
Data-driven Decision Making
Process large, often unstructured, datasets to extract insights to inform decision-making across different business functions to enhance strategic planning and stay ahead in a competitive market.
Process Automation
US AI-powered workflows to automate repetitive and time-consuming tasks – data entry, invoice processing, or employee onboarding, etc. – to reduce errors and increase the speed of operations.
Before this can happen, applications must be modernized to leverage the power of AI services available in the cloud. That’s because legacy applications are often monolithic in scale which makes them difficult to change and integrate with newer services, including generative AI. Migrating these applications to modern architecture like microservices allows organizations to make them more agile and adaptable and ready to take advantage of AI services.