Author name: aiepicentre

Building a RAG chat-based assistant on Amazon EKS Auto Mode and NVIDIA NIMs

Amazon Elastic Kubernetes Service, Amazon OpenSearch Service, Generative AI, Intermediate (200), Technical How-to /

Chat-based assistants powered by Retrieval Augmented Generation (RAG) are transforming customer support, internal help desks, and enterprise search, by delivering fast, accurate answers grounded in your own data. With RAG, you can use a ready-to-deploy foundation model (FM) and enrich it with your own data, making responses relevant and context-aware without the need for fine-tuning […]

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Scalable intelligent document processing using Amazon Bedrock Data Automation

Amazon Augmented AI, Amazon Bedrock, Amazon Bedrock Data Automation, Amazon Comprehend, Amazon SageMaker AI, Amazon SageMaker Ground Truth, Amazon Textract /

Intelligent document processing (IDP) is a technology to automate the extraction, analysis, and interpretation of critical information from a wide range of documents. By using advanced machine learning (ML) and natural language processing algorithms, IDP solutions can efficiently extract and process structured data from unstructured text, streamlining document-centric workflows. When enhanced with generative AI capabilities,

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Whiteboard to cloud in minutes using Amazon Q, Amazon Bedrock Data Automation, and Model Context Protocol

Amazon API Gateway, Amazon Bedrock Data Automation, Amazon Q, Amazon Q Developer /

Upgrading legacy systems has become increasingly important to stay competitive in today’s market as outdated infrastructure can cost organizations time, money, and market position. However, modernization efforts face challenges like time-consuming architecture reviews, complex migrations, and fragmented systems. These delays not only impact engineering teams but have broader impacts including lost market opportunities, reduced competitiveness,

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Bringing agentic Retrieval Augmented Generation to Amazon Q Business

Amazon Machine Learning, Amazon Q, Amazon Q Business, Artificial Intelligence, Generative AI /

Amazon Q Business is a generative AI-powered enterprise assistant that helps organizations unlock value from their data. By connecting to enterprise data sources, employees can use Amazon Q Business to quickly find answers, generate content, and automate tasks—from accessing HR policies to streamlining IT support workflows, all while respecting existing permissions and providing clear citations.

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Empowering students with disabilities: University Startups’ generative AI solution for personalized student pathways

Amazon Bedrock, Amazon Bedrock Agents, Amazon Bedrock Guardrails, Amazon Bedrock Knowledge Bases, Customer Solutions /

This post was co-authored with Laura Lee Williams and John Jabara from University Startups. University Startups, headquartered in Bethesda, MD, was founded in 2020 to empower high school students to expand their education beyond a traditional curriculum. University Startups is focused on special education and related services in school districts throughout the US. After students

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Citations with Amazon Nova understanding models

Amazon Bedrock, Amazon Machine Learning, Amazon Nova, Artificial Intelligence, Intermediate (200) /

Large language models (LLMs) have become increasingly prevalent across both consumer and enterprise applications. However, their tendency to “hallucinate” information and deliver incorrect answers with seeming confidence has created a trust problem. Think of LLMs as you would a human expert: we typically trust experts who can back up their claims with references and walk

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Securely launch and scale your agents and tools on Amazon Bedrock AgentCore Runtime

Amazon Bedrock, Amazon Machine Learning, Artificial Intelligence /

Organizations are increasingly excited about the potential of AI agents, but many find themselves stuck in what we call “proof of concept purgatory”—where promising agent prototypes struggle to make the leap to production deployment. In our conversations with customers, we’ve heard consistent challenges that block the path from experimentation to enterprise-grade deployment: “Our developers want

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PwC and AWS Build Responsible AI with Automated Reasoning on Amazon Bedrock

Amazon Bedrock, Amazon Machine Learning, Customer Solutions, Generative AI /

This is a guest post co-written with Scott Likens, Ambuj Gupta, Adam Hood, Chantal Hudson, Priyanka Mukhopadhyay, Deniz Konak Ozturk, and Kevin Paul from PwC Organizations are deploying generative AI solutions while balancing accuracy, security, and compliance. In this globally competitive environment, scale matters less, speed matters more, and innovation matters most of all, according

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How Amazon scaled Rufus by building multi-node inference using AWS Trainium chips and vLLM

Amazon EC2, Amazon Elastic Container Service, Architecture, AWS Trainium, Customer Solutions /

At Amazon, our team builds Rufus, a generative AI-powered shopping assistant that serves millions of customers at immense scale. However, deploying Rufus at scale introduces significant challenges that must be carefully navigated. Rufus is powered by a custom-built large language model (LLM). As the model’s complexity increased, we prioritized developing scalable multi-node inference capabilities that

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Build an intelligent financial analysis agent with LangGraph and Strands Agents

Amazon Bedrock /

Agentic AI is revolutionizing the financial services industry through its ability to make autonomous decisions and adapt in real time, moving well beyond traditional automation. Imagine an AI assistant that can analyze quarterly earnings reports, compare them against industry expectations, and generate insights about future performance. This seemingly straightforward task involves multiple complex steps: document

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