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How Indegene’s AI-powered social intelligence for life sciences turns social media conversations into insights

This post is co-written with Rudra Kannemadugu and Shravan K S from Indegene Limited. In today’s digital-first world, healthcare conversations are increasingly happening online. Yet the life sciences industry has struggled to keep pace with this shift, facing challenges in effectively analyzing and deriving insights from complex medical discussions on a scale. This post will […]

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Unlock retail intelligence by transforming data into actionable insights using generative AI with Amazon Q Business

Businesses often face challenges in managing and deriving value from their data. According to McKinsey, 78% of organizations now use AI in at least one business function (as of 2024), showing the growing importance of AI solutions in business. Additionally, 21% of organizations using generative AI have fundamentally redesigned their workflows, showing how AI is

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How VideoAmp uses Amazon Bedrock to power their media analytics interface

This post was co-written with Suzanne Willard and Makoto Uchida from VideoAmp. In this post, we illustrate how VideoAmp, a media measurement company, worked with the AWS Generative AI Innovation Center (GenAIIC) team to develop a prototype of the VideoAmp Natural Language (NL) Analytics Chatbot to uncover meaningful insights at scale within media analytics data

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Stream ingest data from Kafka to Amazon Bedrock Knowledge Bases using custom connectors

Retrieval Augmented Generation (RAG) enhances AI responses by combining the generative AI model’s capabilities with information from external data sources, rather than relying solely on the model’s built-in knowledge. In this post, we showcase the custom data connector capability in Amazon Bedrock Knowledge Bases that makes it straightforward to build RAG workflows with custom input

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Automate Amazon EKS troubleshooting using an Amazon Bedrock agentic workflow

As organizations scale their Amazon Elastic Kubernetes Service (Amazon EKS) deployments, platform administrators face increasing challenges in efficiently managing multi-tenant clusters. Tasks such as investigating pod failures, addressing resource constraints, and resolving misconfiguration can consume significant time and effort. Instead of spending valuable engineering hours manually parsing logs, tracking metrics, and implementing fixes, teams should

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