Comprehensive, scalable ML inference architecture using Amazon EKS, leveraging Graviton processors for cost-effective CPU-based inference and GPU instances for accelerated inference. Guidance provides a complete end-to-end platform for deploying LLMs with agentic AI capabilities, including RAG and MCP

This Guidance demonstrates a scalable, serverless approach for automated document processing and information extraction using AWS services, such as Amazon Bedrock Data Automation and Amazon Bedrock foundational models. It combines generative AI and optical character recognition (OCR) to process documents at scale.

This project demonstrates how to build a cost-effective Retrieval-Augmented Generation (RAG) solution using Amazon DynamoDB as a vector store for small use cases, enabling small businesses to implement AI personalization without the high costs typically associated with specialized vector databases.

This Guidance demonstrates how Amazon Bedrock, with offers a range of large language models (LLMs), can perform generative AI-powered analysis on structured and unstructured data sets to support investment analysts.

This Guidance demonstrates how to securely run Model Context Protocol (MCP) servers on the AWS Cloud using containerized architecture. It helps organizations implement industry-standard OAuth 2.0 authentication while protecting server deployments with multiple security layers, including content delivery networks and web application firewalls.

The Open Source 3D Reconstruction Toolbox for Gaussian Splats provides an end-to-end, pipeline-based guidance on AWS to reconstruct 3D scenes or objects from images or video inputs.

Command Line Interface tool for Cloud Intelligence Dashboards deployment

This Guidance demonstrates how to effectively orchestrate multiple specialized AI agents to solve complex customer support challenges through different coordination mechanisms on AWS. Modern customer service environments demand sophisticated handling of multi-step interactions, personalized responses, and seamless access to various data sources.

This Guidance shows how to enhance workforce management through AI-driven automation and real-time intelligence. It demonstrates how to provide staff with quick access to relevant insights, enabling more informed decision-making

This Guidance demonstrates how to overcome data fragmentation challenges by using a team of AI agents to automate the discovery, connection, and analysis of information across siloed systems.