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Enhancing Customer and Agent Experience with AI-Powered Solutions: A Salesforce-Led Market Perspective

eglobalis

This article compares AgentForce with its competitors, focusing on automation, real-time support, and predictive analytics. For example, a North American e-commerce company using Einstein Agent reduced its average case handling time by 30%. I previously mentioned what was coming in AI, and now here we are.

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Dynamic metadata filtering for Amazon Bedrock Knowledge Bases with LangChain

AWS Machine Learning

Amazon Bedrock Knowledge Bases offers a fully managed Retrieval Augmented Generation (RAG) feature that connects large language models (LLMs) to internal data sources. In this post, we discuss using metadata filters with Amazon Bedrock Knowledge Bases. For instructions, see Create an Amazon Bedrock knowledge base.

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How to Lead a B2B CX Transformation Program—And Avoid Costly Mistakes

ECXO

CX transformation often requires breaking entrenched habits and coordinating across silos, which wont happen without active support from the C-suite. While customer delight is the ultimate goal, framing it in terms of ROI and competitive advantage speaks the language of executives and ensures CX strategy gets the necessary support.

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Optimizing AI Agent Experiences: Leading Providers, Gaps, and Human Support Strategies

eglobalis

Optimizing AI Agent Experiences: Leading Providers, Gaps, and Human Support Strategies Introduction Artificial intelligence agents are rapidly transforming customer service and enterprise operations. In practice, the most effective customer experiences blend cutting-edge AI with timely human support.

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How Customer Experience Automation Can Improve Business Performance

InMoment XI

You want to ensure that interactions, whether from emails, SMS messages, chatbots, live support, or any other channel, are connected and tested before the user encounters them. This reduces response times and allows support teams to focus on complex issues. Orchestration refers to creating a cohesive and smooth customer journey.

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Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

These agents excel at automating a wide range of routine and repetitive tasks, such as data entry, customer support inquiries, and content generation. These managed agents play conductor, orchestrating interactions between FMs, API integrations, user conversations, and knowledge sources loaded with your data.

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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning

The Amazon Nova family of models includes Amazon Nova Micro, Amazon Nova Lite, and Amazon Nova Pro, which support text, image, and video inputs while generating text-based outputs. These five webpages act as a knowledge base (source data) to limit the RAG models response.