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Have we reached the end of ‘too expensive’ for enterprise software?

CIO

What began with chatbots and simple automation tools is developing into something far more powerful AI systems that are deeply integrated into software architectures and influence everything from backend processes to user interfaces. While useful, these tools offer diminishing value due to a lack of innovation or differentiation.

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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

AWS Machine Learning - AI

Amazon Q Business , a new generative AI-powered assistant, can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in an enterprises systems. Large-scale data ingestion is crucial for applications such as document analysis, summarization, research, and knowledge management.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

As systems scale, conducting thorough AWS Well-Architected Framework Reviews (WAFRs) becomes even more crucial, offering deeper insights and strategic value to help organizations optimize their growing cloud environments. In this post, we explore a generative AI solution leveraging Amazon Bedrock to streamline the WAFR process.

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Automate emails for task management using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails

AWS Machine Learning - AI

In this post, we demonstrate how to create an automated email response solution using Amazon Bedrock and its features, including Amazon Bedrock Agents , Amazon Bedrock Knowledge Bases , and Amazon Bedrock Guardrails. Solution overview This section outlines the architecture designed for an email support system using generative AI.

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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning - AI

They have structured data such as sales transactions and revenue metrics stored in databases, alongside unstructured data such as customer reviews and marketing reports collected from various channels. The system will take a few minutes to set up your project. On the next screen, leave all settings at their default values.

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Model customization, RAG, or both: A case study with Amazon Nova

AWS Machine Learning - AI

They offer fast inference, support agentic workflows with Amazon Bedrock Knowledge Bases and RAG, and allow fine-tuning for text and multi-modal data. To do so, we create a knowledge base. Complete the following steps: On the Amazon Bedrock console, choose Knowledge Bases in the navigation pane. Choose Next.

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

AWS Machine Learning - AI

One of its key features, Amazon Bedrock Knowledge Bases , allows you to securely connect FMs to your proprietary data using a fully managed RAG capability and supports powerful metadata filtering capabilities. Context recall – Assesses the proportion of relevant information retrieved from the knowledge base.