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How AWS Sales uses generative AI to streamline account planning

AWS Machine Learning

This enables our sales teams to quickly create initial drafts for sections such as customer overviews, industry analysis, and business priorities, which previously required hours of research across the internet and relied on disparate internal AWS tools. Lets explore how we built this AI assistant and some of our future plans.

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From innovation to impact: How AWS and NVIDIA enable real-world generative AI success

AWS Machine Learning

They use a highly optimized inference stack built with NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server to serve both their search application and pplx-api, their public API service that gives developers access to their proprietary models. The results speak for themselvestheir inference stack achieves up to 3.1

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Shaping the future: OMRON’s data-driven journey with AWS

AWS Machine Learning

This approach is particularly powerful when applied to mission-critical data such as enterprise resource planning (ERP) and customer relationship management (CRM) systems because these contain information about internal processes, supply chain management, and customer interactions.

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6 Ways to Vet Gen AI Vendors for the Contact Center

Interactions

There are several ways to work with Gen AI and LLMs, from SaaS applications with embedded Gen AI to custom-built LLMs to applications that bring in Gen AI and LLM capabilities via API. Integration extends the value of your use cases, such as by connecting Gen AI capabilities with your contact center platform, CRM, or analytics/BI tools.

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Top 5 Best Call Center Quality Assurance Software for 2025

Balto

Zoho Desk Zoho Desk is a cloud-based QA platform that enables call centers to manage customer support tickets, customer satisfaction analysis tools, and advanced agent scoring techniques. Text Analysis: Use Qualtrics text analysis capabilities to get deeper insights about survey responses.

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Accelerate client success management through email classification with Hugging Face on Amazon SageMaker

AWS Machine Learning

Email classification project diagram The workflow consists of the following components: Model experimentation – Data scientists use Amazon SageMaker Studio to carry out the first steps in the data science lifecycle: exploratory data analysis (EDA), data cleaning and preparation, and building prototype models.

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10 Essential AI Tools for Multilingual Customer Support in 2025

CSM Magazine

Integration with Existing Customer Support Platforms Seamless integration with existing systems like CRM and ticketing platforms is crucial for streamlined operations and efficient data management. AI-Driven Features Advanced features such as sentiment analysis, automation, and scalability enhance the tools effectiveness.