Category: Application Services

In this post, we present a solution using generative AI and large language models (LLMs) to alleviate the time-consuming and labor-intensive tasks required to build a computer vision application, enabling you to immediately start taking pictures of your asset labels and extract the necessary information to update the inventory using AWS services

In this blog post, we showcase a powerful solution that seamlessly integrates AWS generative AI capabilities in the form of large language models (LLMs) based on Amazon Bedrock into the Office experience. By harnessing the latest advancements in generative AI, we empower employees to unlock new levels of efficiency and creativity within the tools they already use every day.

In this post, we demonstrate Embodied AI Chess with Amazon Bedrock, bringing a new dimension to traditional chess through generative AI capabilities. Our setup features a smart chess board that can detect moves in real time, paired with two robotic arms executing those moves. Each arm is controlled by different FMs—base or custom. This physical implementation allows you to observe and experiment with how different generative AI models approach complex gaming strategies in real-world chess matches.

Seamless integration of customer experience, collaboration tools, and relevant data is the foundation for delivering knowledge-based productivity gains. In this post, we show you how to integrate the popular Slack messaging service with AWS generative AI services to build a natural language assistant where business users can ask questions of an unstructured dataset.

This post discusses how to use AWS Step Functions to efficiently coordinate multi-step generative AI workflows, such as parallelizing API calls to Amazon Bedrock to quickly gather answers to lists of submitted questions. We also touch on the usage of Retrieval Augmented Generation (RAG) to optimize outputs and provide an extra layer of precision, as well as other possible integrations through Step Functions.

While organizations continue to discover the powerful applications of generative AI, adoption is often slowed down by team silos and bespoke workflows. To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle. In the first part of the series, we showed how AI administrators can build a […]

AWS offers powerful generative AI services, including Amazon Bedrock, which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. Many businesses want to integrate these cutting-edge AI capabilities with their existing collaboration tools, such as Google Chat, to […]

In this post, we explore a sample solution that you can use to deploy an image editing application by using AWS serverless services and generative AI services. We use Amazon Bedrock and an Amazon Titan FM that allow you to edit images by using prompts.

In this post, we show you how to use Amazon Transcribe to get near real-time transcriptions of calls sent to Amazon Bedrock for summarization and sensitive data redaction. We’ll walk through an architecture that uses AWS Step Functions to orchestrate the process, providing seamless integration and efficient processing

In this post, we show how to create a multimodal chat assistant on Amazon Web Services (AWS) using Amazon Bedrock models, where users can submit images and questions, and text responses will be sourced from a closed set of proprietary documents.