Category: Technical How-to

Today, we’re excited to announce the integration of Asana AI Studio with Amazon Q index, bringing generative AI directly into your daily workflows. In this post, we explore how Asana AI Studio and Amazon Q index transform enterprise efficiency through intelligent workflow automation and enhanced data accessibility.

In this post, we show how to process multi-page documents with a human review loop using Amazon Bedrock Data Automation and Amazon SageMaker AI.

Amazon Q Business is a fully managed, generative AI-powered assistant that helps enterprises unlock the value of their data and knowledge. With Amazon Q Business, you can quickly find answers to questions, generate summaries and content, and complete tasks by using the information and expertise stored across your company’s various data sources and enterprise systems. […]

In this post, we highlight how you can deploy multiple generative AI models in Amazon Bedrock to instruct an LLM model to create thematic summaries of text responses. We then show how to use multiple LLM models as a jury to review these LLM-generated summaries and assign a rating to judge the content alignment between the summary title and summary description.

In this post, we explore each component in detail, along with the technical implementation of the two core modules: course outline generation and course content generation.

In this post, we present how the Arize AX service can trace and evaluate AI agent tasks initiated through Strands Agents, helping validate the correctness and trustworthiness of agentic workflows.

In this post, we discuss how to implement a low-code no-code AIOps solution that helps organizations monitor, identify, and troubleshoot operational events while maintaining their security posture. We show how these technologies work together to automate repetitive tasks, streamline incident response, and enhance operational efficiency across your organization.

We launched constrained decoding to provide reliability when using tools for structured outputs. Now, tools can be used with Amazon Nova foundation models (FMs) to extract data based on complex schemas, reducing tool use errors by over 95%. In this post, we explore how you can use Amazon Nova FMs for structured output use cases.

In this post, we show how you can build an automated, serverless solution to transform webinar recordings into comprehensive handouts using Amazon Bedrock Data Automation for video analysis. We walk you through the implementation of Amazon Bedrock Data Automation to transcribe and detect slide changes, as well as the use of Amazon Bedrock foundation models (FMs) for transcription refinement, combined with custom AWS Lambda functions orchestrated by AWS Step Functions.

A suspicious transaction report (STR) or suspicious activity report (SAR) is a type of report that a financial organization must submit to a financial regulator if they have reasonable grounds to suspect any financial transaction that has occurred or was attempted during their activities. In this post, we explore a solution that uses FMs available in Amazon Bedrock to create a draft STR.