Customer Stories / Software & Internet / United States

Salesforce Uses AWS Services to Help Customers Deliver AI-Optimized Interactions at Scale
Salesforce’s Data Cloud uses AWS services, machine learning, and generative AI to build a solution that helps companies harmonize their customer data and deliver personalized experiences, all at hyperscale. Salesforce Data Cloud helps customers create a single source of truth for their data with an unprecedented openness in a data platform, a zero ETL framework, and the ability to access data wherever it is. Backed by AWS services including Amazon Elastic Kubernetes Service (Amazon EKS), Amazon EMR and Amazon DynamoDB, the Salesforce Data Cloud created the ability to trigger actions based on any and all data and insight changes, such as a customer’s status change or a propensity score change, and then push out the signals to any downstream applications. Over the last year, Salesforce created a “Bring Your Own AI Model” capability using an integration with Amazon SageMaker that drastically reduces the operational burden to maintain and use AI. Salesforce accesses different large language models (LLMs) from Amazon Bedrock to help customers harmonize data and deliver meaningful AI-optimized interactions at scale.
AWS Services Used
Amazon EKS
Amazon Elastic Kubernetes Service (Amazon EKS) is a managed Kubernetes service to run Kubernetes in the AWS cloud and on-premises data centers..
Amazon EMR
Amazon EMR is the industry-leading cloud big data solution for petabyte-scale data processing, interactive analytics, and machine learning using open-source frameworks.
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Amazon DynamoDB
Amazon DynamoDB is a fully managed, serverless, key-value NoSQL database designed to run high-performance applications at any scale.
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Amazon SageMaker
Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.
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