Artificial Intelligence
Category: Learning Levels
How Indegene’s AI-powered social intelligence for life sciences turns social media conversations into insights
This post explores how Indegene’s Social Intelligence Solution uses advanced AI to help life sciences companies extract valuable insights from digital healthcare conversations. Built on AWS technology, the solution addresses the growing preference of HCPs for digital channels while overcoming the challenges of analyzing complex medical discussions on a scale.
Unlocking enhanced legal document review with Lexbe and Amazon Bedrock
In this post, Lexbe, a legal document review software company, demonstrates how they integrated Amazon Bedrock and other AWS services to transform their document review process, enabling legal professionals to instantly query and extract insights from vast volumes of case documents using generative AI. Through collaboration with AWS, Lexbe achieved significant improvements in recall rates, reaching up to 90% by December 2024, and developed capabilities for broad human-style reporting and deep automated inference across multiple languages.
Demystifying Amazon Bedrock Pricing for a Chatbot Assistant
In this post, we’ll look at Amazon Bedrock pricing through the lens of a practical, real-world example: building a customer service chatbot. We’ll break down the essential cost components, walk through capacity planning for a mid-sized call center implementation, and provide detailed pricing calculations across different foundation models.
Automate enterprise workflows by integrating Salesforce Agentforce with Amazon Bedrock Agents
This post explores a practical collaboration, integrating Salesforce Agentforce with Amazon Bedrock Agents and Amazon Redshift, to automate enterprise workflows.
Responsible AI for the payments industry – Part 1
This post explores the unique challenges facing the payments industry in scaling AI adoption, the regulatory considerations that shape implementation decisions, and practical approaches to applying responsible AI principles. In Part 2, we provide practical implementation strategies to operationalize responsible AI within your payment systems.
Responsible AI for the payments industry – Part 2
In Part 1 of our series, we explored the foundational concepts of responsible AI in the payments industry. In this post, we discuss the practical implementation of responsible AI frameworks.
Discover insights from Microsoft Exchange with the Microsoft Exchange connector for Amazon Q Business
Amazon Q Business is a fully managed, generative AI-powered assistant that helps enterprises unlock the value of their data and knowledge. With Amazon Q Business, you can quickly find answers to questions, generate summaries and content, and complete tasks by using the information and expertise stored across your company’s various data sources and enterprise systems. […]
Cost tracking multi-tenant model inference on Amazon Bedrock
In this post, we demonstrate how to track and analyze multi-tenant model inference costs on Amazon Bedrock using the Converse API’s requestMetadata parameter. The solution includes an ETL pipeline using AWS Glue and Amazon QuickSight dashboards to visualize usage patterns, token consumption, and cost allocation across different tenants and departments.
Building an AI-driven course content generation system using Amazon Bedrock
In this post, we explore each component in detail, along with the technical implementation of the two core modules: course outline generation and course content generation.
Building AIOps with Amazon Q Developer CLI and MCP Server
In this post, we discuss how to implement a low-code no-code AIOps solution that helps organizations monitor, identify, and troubleshoot operational events while maintaining their security posture. We show how these technologies work together to automate repetitive tasks, streamline incident response, and enhance operational efficiency across your organization.