Artificial Intelligence

Category: Amazon Bedrock

Easily build semantic image search using Amazon Titan

Digital publishers are continuously looking for ways to streamline and automate their media workflows to generate and publish new content as rapidly as they can, but without foregoing quality. Adding images to capture the essence of text can improve the reading experience. Machine learning techniques can help you discover such images. “A striking image is […]

Introducing the AWS Generative AI Innovation Center’s Custom Model Program for Anthropic Claude

Since launching in June 2023, the AWS Generative AI Innovation Center team of strategists, data scientists, machine learning (ML) engineers, and solutions architects have worked with hundreds of customers worldwide, and helped them ideate, prioritize, and build bespoke solutions that harness the power of generative AI. Customers worked closely with us to prioritize use cases, […]

Automating product description generation with Amazon Bedrock

In today’s ever-evolving world of ecommerce, the influence of a compelling product description cannot be overstated. It can be the decisive factor that turns a potential visitor into a paying customer or sends them clicking off to a competitor’s site. The manual creation of these descriptions across a vast array of products is a labor-intensive […]

How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

This post was co-written with Greg Benson, Chief Scientist; Aaron Kesler, Sr. Product Manager; and Rich Dill, Enterprise Solutions Architect from SnapLogic. Many customers are building generative AI apps on Amazon Bedrock and Amazon CodeWhisperer to create code artifacts based on natural language. This use case highlights how large language models (LLMs) are able to […]

Heat Map Visualization

Geospatial generative AI with Amazon Bedrock and Amazon Location Service

Today, geospatial workflows typically consist of loading data, transforming it, and then producing visual insights like maps, text, or charts. Generative AI can automate these tasks through autonomous agents. In this post, we discuss how to use foundation models from Amazon Bedrock to power agents to complete geospatial tasks. These agents can perform various tasks […]

Solution architecture diagram

Build a foundation model (FM) powered customer service bot with Amazon Bedrock agents

From enhancing the conversational experience to agent assistance, there are plenty of ways that generative artificial intelligence (AI) and foundation models (FMs) can help deliver faster, better support. With the increasing availability and diversity of FMs, it’s difficult to experiment and keep up-to-date with the latest model versions. Amazon Bedrock is a fully managed service […]

Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

An established financial services firm with over 140 years in business, Principal is a global investment management leader and serves more than 62 million customers around the world. Principal is conducting enterprise-scale near-real-time analytics to deliver a seamless and hyper-personalized omnichannel customer experience on their mission to make financial security accessible for all. They are […]

Harness large language models in fake news detection

Fake news, defined as news that conveys or incorporates false, fabricated, or deliberately misleading information, has been around as early as the emergence of the printing press. The rapid spread of fake news and disinformation online is not only deceiving to the public, but can also have a profound impact on society, politics, economy, and […]

Harnessing the power of enterprise data with generative AI: Insights from Amazon Kendra, LangChain, and large language models

Large language models (LLMs) with their broad knowledge, can generate human-like text on almost any topic. However, their training on massive datasets also limits their usefulness for specialized tasks. Without continued learning, these models remain oblivious to new data and trends that emerge after their initial training. Furthermore, the cost to train new LLMs can […]

Use generative AI to increase agent productivity through automated call summarization

Your contact center serves as the vital link between your business and your customers. Every call to your contact center is an opportunity to learn more about your customers’ needs and how well you are meeting those needs. Most contact centers require their agents to summarize their conversation after every call. Call summarization is a valuable tool that helps contact centers understand and gain insights from customer calls. Additionally, accurate call summaries enhance the customer journey by eliminating the need for customers to repeat information when transferred to another agent. In this post, we explain how to use the power of generative AI to reduce the effort and improve the accuracy of creating call summaries and call dispositions. We also show how to get started quickly using the latest version of our open source solution, Live Call Analytics with Agent Assist.