Artificial Intelligence

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How Nippon India Mutual Fund improved the accuracy of AI assistant responses using advanced RAG methods on Amazon Bedrock

Amazon Bedrock, Amazon Bedrock Guardrails, Amazon Bedrock Knowledge Bases, Amazon Machine Learning, Artificial Intelligence, Customer Solutions /

This post is co-written with Abhinav Pandey from Nippon Life India Asset Management Ltd. Accurate information retrieval through generative AI-powered assistants is a popular use case for enterprises. To reduce hallucination and improve overall accuracy, Retrieval Augmented Generation (RAG) remains the most commonly used method to retrieve reliable and accurate responses that use enterprise data […]

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Optimizing enterprise AI assistants: How Crypto.com uses LLM reasoning and feedback for enhanced efficiency

Amazon Bedrock, Amazon Machine Learning, Artificial Intelligence, Customer Solutions /

This post is co-written with Jessie Jiao from Crypto.com. Crypto.com is a crypto exchange and comprehensive trading service serving 140 million users in 90 countries. To improve the service quality of Crypto.com, the firm implemented generative AI-powered assistant services on AWS. Modern AI assistants—artificial intelligence systems designed to interact with users through natural language, answer

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Build a drug discovery research assistant using Strands Agents and Amazon Bedrock

Advanced (300), Amazon Bedrock, Artificial Intelligence, Life Sciences, Technical How-to /

Drug discovery is a complex, time-intensive process that requires researchers to navigate vast amounts of scientific literature, clinical trial data, and molecular databases. Life science customers like Genentech and AstraZeneca are using AI agents and other generative AI tools to increase the speed of scientific discovery. Builders at these organizations are already using the fully

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How PerformLine uses prompt engineering on Amazon Bedrock to detect compliance violations 

Amazon Bedrock, Amazon Bedrock Prompt Management, Amazon Nova, Artificial Intelligence, AWS Lambda, Compliance, Customer Solutions, Generative AI, Serverless /

This post is co-written with Bogdan Arsenie and Nick Mattei from PerformLine. PerformLine operates within the marketing compliance industry, a specialized subset of the broader compliance software market, which includes various compliance solutions like anti-money laundering (AML), know your customer (KYC), and others. Specifically, marketing compliance refers to adhering to regulations and guidelines set by

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Boost cold-start recommendations with vLLM on AWS Trainium

Artificial Intelligence, AWS Trainium /

Cold start in recommendation systems goes beyond just new user or new item problems—it’s the complete absence of personalized signals at launch. When someone first arrives, or when fresh content appears, there’s no behavioral history to tell the engine what they care about, so everyone ends up in broad generic segments. That not only dampens

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Customize Amazon Nova in Amazon SageMaker AI using Direct Preference Optimization

Amazon Nova, Amazon SageMaker AI, Artificial Intelligence, Foundation models, Intermediate (200) /

At the AWS Summit in New York City, we introduced a comprehensive suite of model customization capabilities for Amazon Nova foundation models. Available as ready-to-use recipes on Amazon SageMaker AI, you can use them to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment. In this

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Enhance generative AI solutions using Amazon Q index with Model Context Protocol – Part 1

Amazon Q, Amazon Q Business, Artificial Intelligence, Best Practices /

Today’s enterprises increasingly rely on AI-driven applications to enhance decision-making, streamline workflows, and deliver improved customer experiences. Achieving these outcomes demands secure, timely, and accurate access to authoritative data—especially when such data resides across diverse repositories and applications within strict enterprise security boundaries. Interoperable technologies powered by open standards like the Model Context Protocol (MCP)

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Beyond accelerators: Lessons from building foundation models on AWS with Japan’s GENIAC program

AI/ML, Amazon Elastic Kubernetes Service, Amazon Machine Learning, Amazon SageMaker HyperPod, Artificial Intelligence, AWS ParallelCluster, Best Practices /

In 2024, the Ministry of Economy, Trade and Industry (METI) launched the Generative AI Accelerator Challenge (GENIAC)—a Japanese national program to boost generative AI by providing companies with funding, mentorship, and massive compute resources for foundation model (FM) development. AWS was selected as the cloud provider for GENIAC’s second cycle (cycle 2). It provided infrastructure

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Streamline deep learning environments with Amazon Q Developer and MCP

Amazon Q, Amazon Q Developer, Artificial Intelligence, AWS Deep Learning AMIs /

Data science teams working with artificial intelligence and machine learning (AI/ML) face a growing challenge as models become more complex. While Amazon Deep Learning Containers (DLCs) offer robust baseline environments out-of-the-box, customizing them for specific projects often requires significant time and expertise. In this post, we explore how to use Amazon Q Developer and Model

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Build an AI-powered automated summarization system with Amazon Bedrock and Amazon Transcribe using Terraform

Amazon Bedrock, Amazon Machine Learning, Amazon Transcribe, Artificial Intelligence, Technical How-to /

Extracting meaningful insights from unstructured data presents significant challenges for many organizations. Meeting recordings, customer interactions, and interviews contain invaluable business intelligence that remains largely inaccessible due to the prohibitive time and resource costs of manual review. Organizations frequently struggle to efficiently capture and use key information from these interactions, resulting in not only productivity

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