Category: Artificial Intelligence

Schneider Electric is a leader in digital transformation in energy management and industrial automation. To effectively manage customer account hierarchies in its CRM at scale, Schneider Electric started leveraging advances in generative artificial intelligence (AI) large language models (LLMs) in April 2023. They created a solution to make timely updates to their customer account hierarchies in their CRM by linking customer account information to the correct parent company based on the latest information retrieved from the Internet and proprietary datasets. In this post, we explore further iterations of this project and how the team applied what they learned to the Salesforce CRM system using Amazon Aurora and Amazon Bedrock.

In this post, we discuss a design for a highly searchable movie content graph database built on Amazon Neptune, a managed graph database service. We demonstrate how to build a list of relevant movies matching a user’s search criteria through the powerful combination of lexical, semantic, and graphical similarity methods using Neptune, Amazon OpenSearch Service, and Neptune Machine Learning. To match, we compare movies with similar text as well as similar vector embeddings. We use both sentence and graph neural network (GNN) models to build these embeddings.

In this post, we explore the key factors to consider when selecting a database for your generative AI applications. We focus on high-level considerations and service characteristics that are relevant to fully managed databases with vector search capabilities currently available on AWS. We examine how these databases differ in terms of their behavior and performance, and provide guidance on how to make an informed decision based on your specific requirements.

In this post, we discuss how to implement Aurora ML performance optimizations to perform real-time inference against a SageMaker endpoint at a large scale. More specifically, we simulate an OLTP workload against the database, where multiple clients are making simultaneous calls against the database and are putting the SageMaker endpoint under stress to respond to thousands of requests in a short time window. Moreover, we show how to use SQL triggers to create an automatic orchestration pipeline for your predictive workload without using additional services.

In this post, we explore how to use Amazon Aurora PostgreSQL and Amazon Bedrock to build Federal Risk and Authorization Management Program (FedRAMP) compliant generative artificial intelligence (AI) applications using Retrieval Augmented Generation (RAG).

Prashant Singh, Chief Operating Officer at LeadSquared, joins Pravin Mittal, Director of Engineering of Amazon Aurora, for a discussion on using generative artificial intelligence (AI) to scale their omnichannel customer service application while controlling costs. LeadSquared helps customers build truly connected, empowered, and self-reliant sales and service organizations, with the power of automation. This Executive […]

LeadSquared is a new-age software as a service (SaaS) customer relationship management (CRM) platform that provides end-to-end sales, marketing, and onboarding solutions. Tailored for sectors like BFSI (banking, financial services, and insurance), healthcare, education, real estate, and more, LeadSquared provides a personalized approach for businesses of every scale. LeadSquared Service CRM goes beyond basic ticketing, […]

Generative artificial intelligence (AI) has reached a turning point, capturing everyone’s imaginations. Integrating generative capabilities into customer-facing services and solutions has become critical. Current generative AI offerings are the culmination of a gradual evolution from machine learning and deep learning models. The leap from deep learning to generative AI is enabled by foundation models. Amazon […]

Amazon DocumentDB (with MongoDB compatibility) offers benefits to customers building modern applications across multiple domains, including healthcare, gaming, and finance. As a fully managed document database, it can improve user experiences through flexibility, scalability, high performance, and advanced functionality. Enterprises that use the JSON data model supported by Amazon DocumentDB can achieve faster application development […]

As organizations continue to scale applications, the need for database developers to quickly pick up new skills becomes increasingly important. Amazon CodeWhisperer is an AI-powered productivity tool for the integrated development environment (IDE) and command line that helps improve developer productivity by generating code recommendations based on your comments in natural language and code in […]