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Unlocking insights and enhancing customer service: Intact’s transformative AI journey with AWS

AWS Machine Learning

In this post, we demonstrate how the CQ solution used Amazon Transcribe and other AWS services to improve critical KPIs with AI-powered contact center call auditing and analytics. Additionally, Intact was impressed that Amazon Transcribe could adapt to various post-call analytics use cases across their organization.

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Enhance customer service efficiency with AI-powered summarization using Amazon Transcribe Call Analytics

AWS Machine Learning

To address these issues, we launched a generative artificial intelligence (AI) call summarization feature in Amazon Transcribe Call Analytics. Simply turn the feature on from the Amazon Transcribe console or using the start_call_analytics_job API. You can upload a call recording in Amazon S3 and start a Transcribe Call Analytics job.

Analytics 120
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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning

For organizations deploying LLMs in production applicationsparticularly in critical domains such as healthcare, finance, or legal servicesthese residual hallucinations pose serious risks, potentially leading to misinformation, liability issues, and loss of user trust. User submits a question When is re:Invent happening this year?,

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How Aetion is using generative AI and Amazon Bedrock to translate scientific intent to results

AWS Machine Learning

In this post, we review how Aetion is using Amazon Bedrock to help streamline the analytical process toward producing decision-grade real-world evidence and enable users without data science expertise to interact with complex real-world datasets. The following diagram illustrates the solution architecture.

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Syngenta develops a generative AI assistant to support sales representatives using Amazon Bedrock Agents

AWS Machine Learning

Agent architecture The following diagram illustrates the serverless agent architecture with standard authorization and real-time interaction, and an LLM agent layer using Amazon Bedrock Agents for multi-knowledge base and backend orchestration using API or Python executors. Domain-scoped agents enable code reuse across multiple agents.

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Speed up your time series forecasting by up to 50 percent with Amazon SageMaker Canvas UI and AutoML APIs

AWS Machine Learning

SageMaker Canvas supports a number of use cases, including time-series forecasting used for inventory management in retail, demand planning in manufacturing, workforce and guest planning in travel and hospitality, revenue prediction in finance, and many other business-critical decisions where highly-accurate forecasts are important.

APIs 120
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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning

Your data remains in the AWS Region where the API call is processed. At the time of writing of this blog, only the AWS Well-Architected Framework, Financial Services Industry, and Analytics lenses have been provisioned. All data is encrypted in transit and at rest.