Category: Technical How-to

This post shows how TP ICAP used Amazon Bedrock Knowledge Bases and Amazon Bedrock Evaluations to build ClientIQ, an enterprise-grade solution with enhanced security features for extracting CRM insights using AI, delivering immediate business value.

In this post, we’ll demonstrate how to implement a Quick Service Restaurants (QSRs) drive-thru solution using Amazon Nova Sonic and AWS services. We’ll walk through building an intelligent system that combines voice AI with interactive menu displays, providing technical insights and implementation guidance to help restaurants modernize their drive-thru operations.

This post provides a comprehensive hands-on guide to fine-tune Amazon Nova Lite for document processing tasks, with a focus on tax form data extraction. Using our open-source GitHub repository code sample, we demonstrate the complete workflow from data preparation to model deployment. 

In this post, we explore how Amazon Bedrock AgentCore Memory transforms raw conversational data into persistent, actionable knowledge through sophisticated extraction, consolidation, and retrieval mechanisms that mirror human cognitive processes. The system tackles the complex challenge of building AI agents that don’t just store conversations but extract meaningful insights, merge related information across time, and maintain coherent memory stores that enable truly context-aware interactions.

Misconfiguration issues in distributed training with Amazon EKS can be prevented following a systematic approach to launch required components and verify their proper configuration. This post walks through the steps to set up and verify an EKS cluster for training large models using DLCs.

This post provides a comprehensive guide on integrating the Almond kernel into SageMaker Studio, offering a solution for Scala development within the platform.

In this post, we explore how to build a conversational device management system using Amazon Bedrock AgentCore. With this solution, users can manage their IoT devices through natural language, using a UI for tasks like checking device status, configuring WiFi networks, and monitoring user activity.

In this post, we demonstrate the development of a conceptual Medical Reports Analysis Dashboard that combines Amazon Bedrock AI capabilities, LangChain’s document processing, and Streamlit’s interactive visualization features. The solution transforms complex medical data into accessible insights through a context-aware chat system powered by large language models available through Amazon Bedrock and dynamic visualizations of health parameters.

In this post, we explore how Amazon Quick Suite’s Model Context Protocol (MCP) client enables secure, standardized connections to enterprise applications and AI agents, eliminating the need for complex custom integrations. You’ll discover how to set up MCP Actions integrations with popular enterprise tools like Atlassian Jira and Confluence, AWS Knowledge MCP Server, and Amazon Bedrock AgentCore Gateway to create a collaborative environment where people and AI agents can seamlessly work together across your organization’s data and applications.

In this post, we demonstrate how to integrate Amazon SageMaker HyperPod with Anyscale platform to address critical infrastructure challenges in building and deploying large-scale AI models. The combined solution provides robust infrastructure for distributed AI workloads with high-performance hardware, continuous monitoring, and seamless integration with Ray, the leading AI compute engine, enabling organizations to reduce time-to-market and lower total cost of ownership.