AWS Machine Learning Blog
Tag: Retail
Build an ecommerce product recommendation chatbot with Amazon Bedrock Agents
In this post, we show you how to build an ecommerce product recommendation chatbot using Amazon Bedrock Agents and foundation models (FMs) available in Amazon Bedrock.
Analyze customer reviews using Amazon Bedrock
This post explores an innovative application of large language models (LLMs) to automate the process of customer review analysis. LLMs are a type of foundation model (FM) that have been pre-trained on vast amounts of text data. This post discusses how LLMs can be accessed through Amazon Bedrock to build a generative AI solution that automatically summarizes key information, recognizes the customer sentiment, and generates actionable insights from customer reviews. This method shows significant promise in saving human analysts time while producing high-quality results. We examine the approach in detail, provide examples, highlight key benefits and limitations, and discuss future opportunities for more advanced product review summarization through generative AI.
Build an AI-powered virtual agent for Genesys Cloud using QnABot and Amazon Lex
The rise of artificial intelligence technologies enables organizations to adopt and improve self-service capabilities in contact center operations to create a more proactive, timely, and effective customer experience. Voice bots, or conversational interactive voice response systems (IVR), use natural language processing (NLP) to understand customers’ questions and provide relevant answers. Businesses can automate responses to […]
Enhancing customer service experiences using Conversational AI: Power your contact center with Amazon Lex and Genesys Cloud
Customers expect personalized contact center experiences. They want easy access to customer support and quick resolution of their issues. Delighting callers with a quick and easy interaction remains central to the customer experience (CX) strategy for support organizations. Enterprises often deploy omni-channel contact centers so that they can provide simple mechanisms for their customers to […]