Fine-tune LLM with Databricks Unity Catalog and Amazon SageMaker AI

Securing AI agents: How AWS and Cisco AI Defense scale MCP and A2A deployments

Artificial Intelligence Securing AI agents: How AWS and Cisco AI Defense scale MCP and A2A deployments Model Context Protocol (MCP) adoption has accelerated rapidly since its introduction in November 2024. Enterprises now manage dozens to hundreds of MCP servers—tools that extend AI agent capabilities by connecting them to external data sources and APIs. The Agent-to-Agent (A2A) Protocol followed in April 2025, enabling autonomous agents to communicate directly without human intervention. More recently, Agent Skills emerged across enterprise infrastructure. This growth has created three security gaps: teams lack visibility into which tools and agents are deployed, manual security reviews can’t scale to match deployment velocity, and compliance frameworks require audit trails that don’t exist for autonomous AI agents. Organizations face risks from unvetted MCP servers, A2A agents, and Skills: inadvertent access to sensitive data systems, compliance violations under SOX and GDPR…

Build real-time voice streaming applications with Amazon Nova Sonic and WebRTC

Artificial Intelligence Build real-time voice streaming applications with Amazon Nova Sonic and WebRTC Building end-to-end live streaming applications with real-time voice interaction presents several challenges: network bandwidth constraints can cause high latency and quality degradation in time-critical applications. Language barriers limit effective human-machine interaction in multilingual voice communication. Scalability and resilience require a difficult balance between performance and infrastructure costs. Cross-browser and mobile compatibility demands significant development effort, especially for startups. This post introduces a solution based on Amazon Nova 2 Sonic (Nova Sonic) and Amazon Kinesis Video Streams WebRTC (WebRTC) that addresses these challenges. WebRTC is responsible for dynamically adjusting the bitrate in unstable networks, which helps to maintain audio quality while reducing dropped connections. Nova Sonic provides effective human language dialogues, so users can interact more naturally in their chosen language. Both services are fully managed by AWS,…

Build financial document processing with Pulse AI and Amazon Bedrock

Artificial Intelligence Build financial document processing with Pulse AI and Amazon Bedrock Financial institutions process thousands of complex documents daily. Optical Character Recognition (OCR) errors in financial data can propagate through interconnected calculations, affecting analytical accuracy. While a single OCR error in a standard legal document might require only a quick manual correction, the same mistake in financial data can cascade through interconnected calculations, leading to systematic errors in analysis and potentially costly to organizations. Traditional OCR tools fall critically short when processing the complex financial documents that institutions handle daily—balance sheets, income statements, SEC filings, research reports, and audit materials. These documents feature intricate table structures with merged cells and hierarchical data, multi-column layouts with interconnected references, and context-dependent information requiring semantic understanding. Traditional OCR approaches treat these documents as images, missing the structural relationships and contextual nuances that…

Navigating EU AI Act requirements for LLM fine-tuning on Amazon SageMaker AI

Artificial Intelligence Navigating EU AI Act requirements for LLM fine-tuning on Amazon SageMaker AI The EU AI Act requires organizations fine-tuning large language models (LLMs) to track computational resources measured in floating-point operations (FLOPs) to determine compliance obligations. As customers increasingly fine-tune LLMs for domain-specific use cases, we hear a common question: how do I know if my training job triggers new regulatory obligations? Amazon SageMaker AI provides a managed machine learning (ML) service for building, training, and deploying models. This solution uses Amazon SageMaker Training jobs to run fine-tuning workloads on fully managed infrastructure. SageMaker Training jobs handle resource provisioning, scaling, and cluster management, with built-in support for distributed training, integration with AWS CloudTrail and Amazon CloudWatch for governance, and automatic decommissioning of compute resources after training completes. The Fine-Tuning FLOPs Meter extends these capabilities with purpose-built compliance tracking…

Automate schema generation for intelligent document processing

Artificial Intelligence Automate schema generation for intelligent document processing Before you can extract information from documents using intelligent document processing (IDP) techniques, you need a schema for each document class that defines what to extract. But how do you create schemas when you have thousands of documents and don’t know what classes exist? Doing this at scale can take substantial manual effort, making downstream IDP initiatives difficult to justify. In this post, we’ll show you how our multi-document discovery feature solves this problem. It serves as an automated pre-processing step, analyzing unknown documents, clustering them by type, and generating schemas ready for the IDP Accelerator. You’ll learn how the new capability uses visual embeddings for automatic clustering and agents for schema generation. We’ll also walk you through running the solution on your own document collections. IDP Accelerator The IDP Accelerator…

How Amazon Finance streamlines regulatory inquiries by using generative AI on AWS

Artificial Intelligence How Amazon Finance streamlines regulatory inquiries by using generative AI on AWS Amazon’s Finance Technology (FinTech) teams build and operate systems for Amazon teams to manage regulatory inquiries in compliance with different jurisdictions. These teams process regulatory inquiries from authorities, each presenting different requirements, document formats, and complexity levels. Processing these regulatory inquiries involves reviewing documentation, extracting relevant information, retrieving supporting data from multiple systems within Amazon’s infrastructure, and compiling responses within regulatory timeframes. As inquiry frequency and business complexity grew, Amazon needed a more scalable approach. In this post, we demonstrate how Amazon FinTech teams are using Amazon Bedrock and other AWS services to build a scalable AI application to transform how regulatory inquiries are handled. Each team using this solution creates and maintains its own dedicated knowledge base, populated with that team’s specific documents and reference…

Introducing Claude Platform on AWS: Anthropic’s native platform, through your AWS account

Artificial Intelligence Introducing Claude Platform on AWS: Anthropic’s native platform, through your AWS account Today, we’re excited to announce the general availability of Claude Platform on AWS. Claude Platform on AWS is a new service that gives customers direct access to Anthropic’s native Claude Platform experience through their AWS account, with no separate credentials, contracts, or billing relationships required. AWS is the first cloud provider to offer access to the native Claude Platform experience. In this post, we explore how Claude Platform on AWS works and how you can start using it today. Claude Platform experience through AWS With Claude Platform on AWS, you work with the same APIs, features, and console experience available through Anthropic directly. This includes the Messages API, Claude Managed Agents (beta), advisor tool (beta), web search and web fetch, MCP connector (beta), Agent Skills (beta),…

Building web search-enabled agents with Strands and Exa

Artificial Intelligence Building web search-enabled agents with Strands and Exa This post is co written by Ishan Goswami and Nitya Sridhar from Exa. If you are building web search-enabled AI agents for research, fact-checking, or competitive intelligence, access to current and reliable information is critical. Most general-purpose search APIs are not designed for agent workflows. They return HTML-heavy pages and short snippets optimized for human browsing, not structured data that an agent can directly consume. As a result, developers often need to build additional layers, custom crawlers, parsers, and ranking logic, to transform this content into something usable within an agent workflow. The Exa integration for the Strands Agents SDK addresses this gap with an AI-native search and retrieval layer built directly into the tool interface. Exa delivers clean, structured content formatted for direct use in LLM context windows, without…

Amazon Quick: Accelerating the path from enterprise data to AI-powered decisions

Artificial Intelligence Amazon Quick: Accelerating the path from enterprise data to AI-powered decisions Enterprise data with tens of millions of rows, row-level and column-level security, and dozens of datasets spanning multiple business domains need AI-generated answers that are trustworthy, reproducible, and fast, while respecting governance rules consistently. With foundation models (FMs), organizations can build systems that work well for small datasets where a business user asks a question about their data and gets an answer in seconds. Amazon Quick can also help turn your large enterprise data into fast and accurate AI-powered decisions. In this post, you will learn about five new capabilities of Amazon Quick that accelerate how data professionals deliver trusted AI-powered insights at enterprise scale. Dataset Q&A: Talk to your data directly When a VP asks, “How is churn trending for this product?“, getting that answer means…

How Miro uses Amazon Bedrock to boost software bug routing accuracy and improve time-to-resolution from days to hours

Artificial Intelligence How Miro uses Amazon Bedrock to boost software bug routing accuracy and improve time-to-resolution from days to hours This post is co-authored with Philipp Pavlov, Dmytro Romantsov, Evgeny Mironenko, and Gowri Suryanarayana from Miro. Miro is an AI-powered innovation workspace that serves over 95 million users globally, helping teams transform unstructured ideas into organized workflows. To support this scale and continue enhancing their system, Miro’s developer experience team decided to create an innovation workspace for Miro itself, using modern technologies to boost developer productivity. One of the key challenges faced by the team is efficiently routing software bugs to the responsible teams. Quick and accurate bug routing removes unnecessary context-switching, reduces developer frustration, improves time-to-resolution, and ultimately leads to a better product and happier customers. At Miro, a significant percentage of bugs miss internal resolution SLAs primarily due…

Manufacturing intelligence with Amazon Nova Multimodal Embeddings

Artificial Intelligence Manufacturing intelligence with Amazon Nova Multimodal Embeddings If you work in aerospace, automotive, or heavy industry manufacturing, your organization likely maintains vast repositories of technical documents. These documents combine written specifications with engineering diagrams, CAD drawings, inspection photographs, thermal analysis plots, and fatigue curves. A text query about maximum wall temperature at the nozzle throat might have its answer locked inside a thermal contour plot rather than written prose. Text-only retrieval systems can’t surface that information because they don’t see the image content. Amazon Nova Multimodal Embeddings addresses this gap by mapping text, images, and document pages into a shared vector space. A text query can retrieve an engineering diagram, and an image query can retrieve a written specification, because both modalities share the same coordinate system. In this post, we build a multimodal retrieval system for aerospace…

Halliburton enhances seismic workflow creation with Amazon Bedrock and Generative AI

Artificial Intelligence Halliburton enhances seismic workflow creation with Amazon Bedrock and Generative AI Seismic data analysis is an essential component of energy exploration, but configuring complex processing workflows has traditionally been a time-consuming and error-prone challenge. Halliburton’s Seismic Engine, a cloud-native application for seismic data processing, is a powerful tool that previously required manual configuration of approximately 100 specialized tools to create workflows. This process was not only time-consuming but also required deep expertise, potentially limiting the accessibility and efficiency of the software. To address this challenge, Halliburton partnered with the AWS Generative AI Innovation Center to develop an AI-powered assistant for Seismic Engine. The solution uses Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon Nova, and Amazon DynamoDB to transform complex workflow creation into conversations. Geoscientists and data scientists can configure processing tools through natural language interaction instead of manual…

Secure short-term GPU capacity for ML workloads with EC2 Capacity Blocks for ML and SageMaker training plans

Artificial Intelligence Secure short-term GPU capacity for ML workloads with EC2 Capacity Blocks for ML and SageMaker training plans As companies of various sizes adopt graphic processing units (GPU)-based machine learning (ML) training, fine-tuning and inference workloads, the demand for GPU capacity has outpaced industry-wide supply. This imbalance has made GPUs a scarce resource, creating a challenge for customers who need reliable access to GPU compute resources for their ML workloads. When you encounter GPU capacity limitations, you might consider creating on-demand capacity reservations (ODCRs). ODCRs apply to planned, steady-state workloads with well-understood usage patterns. Short-term ODCR availability for GPU instances, particularly P-type instances, is often limited. Additionally, without a long-term contract, ODCRs are billed at on-demand rates, offering no cost advantage. This makes ODCRs unsuitable for short or exploratory workloads such as testing, evaluations, or events. A guided approach…

Overcoming reward signal challenges: Verifiable rewards-based reinforcement learning with GRPO on SageMaker AI

Artificial Intelligence Overcoming reward signal challenges: Verifiable rewards-based reinforcement learning with GRPO on SageMaker AI Training large language models requires accurate feedback signals, but traditional reinforcement learning (RL) often struggles with reward signal reliability. The quality of these signals directly influences how models learn and make decisions. However, creating robust feedback mechanisms can be complex and error prone. Real-world training scenarios often introduce hidden biases, unintended incentives, and ambiguous success criteria that can derail the learning process, leading to models that behave unpredictably or fail to meet desired objectives. In this post, you will learn how to implement reinforcement learning with verifiable rewards (RLVR) to introduce verification and transparency into reward signals to improve training performance. This approach works best when outputs can be objectively verified for correctness, such as in mathematical reasoning, code generation, or symbolic manipulation tasks. You…

Agents that transact: Introducing Amazon Bedrock AgentCore payments, built with Coinbase and Stripe

Artificial Intelligence Agents that transact: Introducing Amazon Bedrock AgentCore payments, built with Coinbase and Stripe We’re in the midst of a fundamental shift in how software gets built and used. AI agents are moving beyond assistants that wait for instructions. They call APIs, access MCP servers, coordinate with other agents, and complete complex multi-step tasks on behalf of users. As agents take on increasingly diverse tasks, the ecosystem around them is expanding just as fast to meet that demand. Looking further ahead, services, tools, and content must be designed for humans and agents. Agents will discover, evaluate, and pay for resources when they need, all within a single execution loop. The services that support them must be priced and consumed in that way: fractions of a cent per call, billed in real time. Early protocols like x402, ACP, MPP, and…

Cost effective deployment of vision-language models for pet behavior detection on AWS Inferentia2

Artificial Intelligence Cost effective deployment of vision-language models for pet behavior detection on AWS Inferentia2 Tomofun, the Taiwan-headquartered pet-tech startup behind the Furbo Pet Camera, is redefining how pet owners interact with their pets remotely. Furbo combines smart cameras with AI to detect behaviors such as barking, running, or unusual activity, and alerts owners in real time. At the core of this capability are computer vision and vision-language models that interpret pet actions from the video streams. Originally, Furbo’s inference workloads were hosted on GPU-based Amazon Elastic Compute Cloud (Amazon EC2) instances. While GPUs provided high throughput, they were also costly because the always-on inference needed to support real-time pet activity alerts at scale. To reduce costs and maintain accuracy, Tomofun turned to EC2 Inf2 instances powered by AWS Inferentia2, the Amazon purpose-built AI chips. In this post, we walk…

Intelligence-driven message defense and insights using Amazon Bedrock

Artificial Intelligence Intelligence-driven message defense and insights using Amazon Bedrock Direct communication between buyers and sellers outside approved channels can result in significant revenue loss annually while severely damaging brand reputation and destroying valuable business relationships. While messaging systems are essential for modern business operations and help provide rich customer insights, they can create significant risks when parties bypass the brokerage system to communicate directly. When buyers and sellers exchange contact information and take their transactions offline, brokerages can not only lose immediate revenue but also suffer long-term damage as their marketplace value diminishes. This challenge is particularly acute in brokerage businesses where the service’s core value lies in facilitating secure, reliable connections between parties. While in-application messaging enables important transaction details, such as delivery placement “leave it by the back door” or specific times “only deliver after 4:00 PM”,…

Secure AI agents with Amazon Bedrock AgentCore Identity on Amazon ECS

Artificial Intelligence Secure AI agents with Amazon Bedrock AgentCore Identity on Amazon ECS AI agents in production require secure access to external services. Amazon Bedrock AgentCore Identity, available as a standalone service, secures how your AI agents access external services whether they run on compute platforms like Amazon ECS, Amazon EKS, AWS Lambda, or on-premises. An earlier post covered AgentCore Identity credential management for AI agents. Running agents on compute environments like ECS raises two questions: How to build an application-owned Session Binding endpoint, and how to manage workload access token lifecycle? This post implements Authorization Code Grant (3-legged OAuth) on Amazon ECS with secure session binding and scoped tokens. This post provides a working implementation with: - Secure session binding that prevents CSRF and browser-swapping attacks - Auth tokens scoped to each user session, following least-privilege principles - Separation…

Introducing OS Level Actions in Amazon Bedrock AgentCore Browser

Artificial Intelligence Introducing OS Level Actions in Amazon Bedrock AgentCore Browser AI agents that automate web workflows operate within the browser’s web layer, the DOM that Playwright and the Chrome DevTools Protocol (CDP) expose. AgentCore Browser provides a secure, isolated browser environment for this, and it works well for the vast majority of automation: navigating pages, filling forms, clicking elements, extracting content. But the web layer has a hard boundary. Anything that the operating system renders (native dialogs, security prompts, certificate choosers, context menus, even Chrome settings) sits outside the DOM entirely. CDP can’t see it, and Playwright can’t interact with it. When a web application calls window.print() and a system print dialog appears, Playwright has no DOM to interact with. When a workflow requires a keyboard shortcut or a right-click context menu, CDP has no mechanism to issue those…

Streamlining generative AI development with MLflow v3.10 on Amazon SageMaker AI

Artificial Intelligence Streamlining generative AI development with MLflow v3.10 on Amazon SageMaker AI Today, we’re excited to announce that Amazon SageMaker AI MLflow Apps now support MLflow version 3.10, bringing enhanced capabilities for generative AI development and streamlined experiment tracking to your generative AI workflows. Building on the foundations established with Amazon SageMaker AI MLflow Apps, this latest version introduces powerful new features for observability, evaluation, and generative AI development that help data scientists and ML engineers accelerate their AI initiatives from experimentation to production. In this post, we’ll explore what’s new in MLflow v3.10, walk you through getting started with SageMaker AI MLflow Apps, and how to leverage these enhancements to build generative AI applications. What’s new in MLflow v3.10 MLflow 3.10 introduces a set of targeted improvements to the MLflow ecosystem that extend the tracing and observability capabilities…

How Hapag-Lloyd uses Amazon Bedrock to transform customer feedback into actionable insights

Artificial Intelligence How Hapag-Lloyd uses Amazon Bedrock to transform customer feedback into actionable insights Hapag-Lloyd stands as one of the world’s leading liner shipping companies, operating a modern fleet of 313 container ships with a total transport capacity of 2.5 million TEU (Twenty-foot Equivalent Unit—a standard unit of measurement for cargo capacity in container shipping). The company maintains a container capacity of 3.7 million TEU, which includes one of the industry’s largest and most modern fleets of reefer containers. With approximately 14,000 employees in the Liner Shipping Segment and more than 400 offices spread across 140 countries, Hapag-Lloyd maintains a robust global presence. Through 133 liner services worldwide, we facilitate reliable connections between more than 600 ports across the continents. The company’s Digital Customer Experience and Engineering team, distributed between Hamburg and Gdańsk, drives digital innovation by developing and maintaining…

Capacity-aware inference: Automatic instance fallback for SageMaker AI endpoints

Artificial Intelligence Capacity-aware inference: Automatic instance fallback for SageMaker AI endpoints As organizations scale generative AI workloads in production, securing reliable GPU compute has become one of the most persistent operational challenges. Large language models (LLMs) and multimodal architectures demand specific instance types and when that capacity isn’t available, endpoints fail before they serve a single request. Building a real-time inference endpoint on Amazon SageMaker AI has meant committing to a single instance type at creation time. When that type had insufficient capacity, the endpoint failed to reach a running state. You updated your configuration, selected a different instance type, and retried repeating the cycle until a provisioning attempt succeeded. Today, Amazon SageMaker AI introduces capacity aware instance pool for new and existing inference endpoints. You define a prioritized list of instance types, and SageMaker AI automatically works through your…

Introducing Dataset Q&A: Expanding natural language querying for structured datasets in Amazon Quick

Artificial Intelligence Introducing Dataset Q&A: Expanding natural language querying for structured datasets in Amazon Quick Every BI team knows this bottleneck: a business user has a question that falls outside existing dashboards, so they file a ticket. An analyst writes the query, validates the results, and delivers them—hours or days later. Multiply that by hundreds of ad-hoc requests per month, and the backlog becomes the single biggest constraint on data team productivity. Amazon Quick now adds a powerful new natural language query capability, Dataset Q&A, to remove this bottleneck. Your question is translated into SQL, run against the full dataset, and the results are returned in seconds—no row sampling, topic curation, or pre-configured calculated fields required. Quick already offers two natural language querying modes. Dashboard Q&A is intended for questions about data visualized in published dashboards, drawing on the business…

From data lake to AI-ready analytics: Introducing new data source with S3 Tables in Amazon Quick

Artificial Intelligence From data lake to AI-ready analytics: Introducing new data source with S3 Tables in Amazon Quick Organizations today are increasingly looking to combine analytics and AI to accelerate insights and decision-making. Amazon Quick, a unified agentic AI-powered analytics and decision intelligence service, brings together data visualization, natural language interaction, and agent-driven automation in a single, governed experience. With this, business users can explore data, generate insights, and take action without requiring specialized machine learning (ML) expertise. At the same time, modern data architectures are evolving toward scalable data lakes built on open table formats such as Apache Iceberg, which offer improved performance, cost efficiency, and governance. However, analyzing large-scale data often requires moving it into data warehouses or OLAP systems, introducing latency, added cost, and operational complexity. Although existing query modes—such as Direct Query and SPICE (Super-fast, Parallel,…

Generate dashboards from natural language prompts in Amazon Quick

Artificial Intelligence Generate dashboards from natural language prompts in Amazon Quick Building meaningful dashboards demands hours of manual setup, even for experienced BI professionals. Amazon Quick now generates complete multi-sheet dashboards from natural language prompts, taking you from one or more datasets to a production-ready analysis in minutes. Data analysts building recurring operations reports, program managers preparing a leadership review, or engineers exploring a new dataset can describe what they want, and Amazon Quick produces multiple organized sheets with visuals selected for your data, filter controls for stakeholders to explore by different dimensions, and calculated fields such as year-over-year growth and month-over-month comparisons. Before generating, you review and edit an interactive plan of the proposed structure, keeping you in control of the final output. In Amazon Quick, Analysis is the authoring surface where you build and arrange visuals, filters, and…

Agent-guided workflows to accelerate model customization in Amazon SageMaker AI

Artificial Intelligence Agent-guided workflows to accelerate model customization in Amazon SageMaker AI Every organization has access to the same foundation models. The real competitive advantage comes from customizing them with your proprietary data and domain expertise. But getting there is complex, even for experienced teams. It requires mastering fine-tuning techniques like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning Verifiable Rewards (RLVR), navigating fragmented APIs and model-specific data formats, designing rigorous evaluations, and managing months-long experiment cycles. Amazon SageMaker AI now offers an agentic experience that changes this. Developers describe their use case using natural language, and the AI coding agent streamlines the entire journey, from use case definition and data preparation through technique selection, evaluation, and deployment. Purpose-built agent skills deliver specialized expertise on fine-tuning applied to your specific use case, data transformation to required formats, quality…

Introducing agent quality optimization in AgentCore, now in preview

Artificial Intelligence Introducing agent quality optimization in AgentCore, now in preview Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were never designed for. Agent quality quietly degrades. In most teams, the improvement process still looks the same: without automatic feedback loops, when a user complains, a developer reads through traces, forms a hypothesis, rewrites the prompt, tests a handful of cases, and ships the fix. Then the cycle repeats, often introducing a new issue for a different user. Up until today, Amazon Bedrock AgentCore provided the pieces for you to debug it manually or build custom implementations: check the evaluation scores to detect quality drop, deep…

Beyond BI: How the Dataset Q&A feature of Amazon Quick powers the next generation of data decisions

Artificial Intelligence Beyond BI: How the Dataset Q&A feature of Amazon Quick powers the next generation of data decisions Business leaders across industries rely on operational dashboards as the shared source of truth that their teams execute against daily. But dashboards are built to answer known questions. When teams need to explore further, ad-hoc, multi-dimensional, or unforeseen questions, they hit a bottleneck. They wait hours or days for BI teams to build new views or update reports. The Dataset Q&A feature bridges that gap. You can ask questions in natural language, get accurate answers in seconds, with no new dashboards to build, and no queue to wait in. Just an interactive conversation with your existing datasets, without disrupting the dashboards your teams already depend on. The challenge AWS customers expect fast, informed support when they’re evaluating new technologies, troubleshooting production…

AWS Transform now automates BI migration to Amazon Quick in days

Artificial Intelligence AWS Transform now automates BI migration to Amazon Quick in days Migrating to Amazon Quick doesn’t have to mean starting from scratch. Your dashboards encode hard-won domain knowledge: calculated fields your analysts perfected, layouts your executives rely on every Monday morning, security rules tuned to your org chart. You want AI-powered insights and serverless scale, but you’re staring at hundreds of dashboards and a migration estimate measured in months. Now you can significantly accelerate your migration to Amazon Quick, potentially reducing timelines from months to days. In this post, we walk through the full journey, from setting up your migration workspace in AWS Transform to subscribing to partner agents through AWS Marketplace to unlocking Amazon Quick capabilities that change how your organization consumes data. The real cost of staying on legacy BI If you’re running a legacy BI…

Unleashing Agentic AI Analytics on Amazon SageMaker with Amazon Athena and Amazon Quick

Artificial Intelligence Unleashing Agentic AI Analytics on Amazon SageMaker with Amazon Athena and Amazon Quick Modern enterprises face mounting challenges in extracting actionable insights from vast data lakes and lakehouses spanning petabytes of structured and unstructured data. Traditional analytics require specialized technical expertise in SQL, data modeling, and business intelligence tools, creating bottlenecks that slow decision-making across retail, financial services, healthcare, Travel & Hospitality, manufacturing and many more industries. This architecture demonstrates how agentic AI assistant from Amazon Quick transform data analytics into a self-service capability. It showcases enabling business users to query complex structured datasets and mix with unstructured data to find the valuable insights to improve their business outcomes through intuitive natural language interfaces. To demonstrate the functionality, we built a lakehouse using the TPC-H datasets as our foundation. This integrated architecture leverages Amazon Simple Storage Service (Amazon…

Configuring Amazon Bedrock AgentCore Gateway for secure access to private resources

Artificial Intelligence Configuring Amazon Bedrock AgentCore Gateway for secure access to private resources AI agents in production environments often need to reach internal APIs, databases, and private resources that sit behind Amazon Virtual Private Cloud (Amazon VPC) boundaries. Managing private connectivity for each agent-to-tool path adds operational overhead and slows deployment. Amazon Bedrock AgentCore VPC connectivity is designed to deploy AI agents and Model Context Protocol (MCP) servers without requiring the network traffic to be exposed to the public internet. This capability extends to managed Amazon VPC egress for Amazon Bedrock AgentCore Gateway, so you can connect to endpoints inside private networks across your AWS environment. In this post, you will configure Amazon Bedrock AgentCore Gateway to access private endpoints using Resource Gateway, a managed construct that provisions Elastic Network Interfaces (ENIs) directly inside your Amazon VPC, one per subnet.…

Sun Finance automates ID extraction and fraud detection with generative AI on AWS

Artificial Intelligence Sun Finance automates ID extraction and fraud detection with generative AI on AWS This post was co-authored with Krišjānis Kočāns, Kaspars Magaznieks, Sergei Kiriasov from Sun Finance Group If you process identity documents at scale—loan applications, account openings, compliance checks—you’ve likely hit the same wall: traditional optical character recognition (OCR) gets you partway there, but extraction errors still push a large share of applications into manual review queues. Add fraud detection to the mix, and the manual workload compounds. Sun Finance, a Latvian fintech founded in 2017, operates as a technology-first online lending marketplace across nine countries. The company processes a new loan request every 0.63 seconds and delivers more than 4 million evaluations monthly. In one of their highest-volume industries, with 80,000 monthly applications for microloans, approximately 60% of applications required manual operator review. Sun Finance partnered…

AWS Generative AI Model Agility Solution: A comprehensive guide to migrating LLMs for generative AI production

Artificial Intelligence AWS Generative AI Model Agility Solution: A comprehensive guide to migrating LLMs for generative AI production Maintaining model agility is crucial for organizations to adapt to technological advancements and optimize their artificial intelligence (AI) solutions. Whether transitioning between different large language model (LLM) families or upgrading to newer versions within the same family, a structured migration approach and a standardized process are essential for facilitating continuous performance improvement while minimizing operational disruptions. However, developing such a solution is challenging in both technical and non-technical aspects because the solution needs to: - Be generic to cover a variety of use cases - Be specific so that a new user can apply it to the target use case - Provide comprehensive and fair comparison between LLMs - Be automated and scalable - Incorporate domain- and task-specific knowledge and inputs -…

Reinforcement fine-tuning with LLM-as-a-judge

Artificial Intelligence Reinforcement fine-tuning with LLM-as-a-judge Large language models (LLMs) now drive the most advanced conversational agents, creative tools, and decision-support systems. However, their raw output often contains inaccuracies, policy misalignments, or unhelpful phrasing—issues that undermine trust and limit real-world utility. Reinforcement Fine‑Tuning (RFT) has emerged as the preferred method to align these models efficiently, using automated reward signals to replace costly manual labeling. At the heart of modern RFT is reward functions. They’re built for each domain through verifiable reward functions that can score LLM generations through a piece of code (Reinforcement Learning with Verifiable Rewards or RLVR) or with LLM-as-a-judge, where a separate language model evaluates candidate responses to guide alignment (Reinforcement Learning with AI Feedback or RLAIF). Both these methods provide scores to the RL algorithm to nudge the model to solve the problem at hand. In…

Run custom MCP proxies serverless on Amazon Bedrock AgentCore Runtime

Artificial Intelligence Run custom MCP proxies serverless on Amazon Bedrock AgentCore Runtime When AI agents connect to tools through the Model Context Protocol (MCP), they gain access to capabilities that range from database queries and API calls to file operations and third-party service integrations. In production, these interactions need proper governance, controls, and observability aligned with an organization’s security policies. This includes sanitizing tool inputs before they reach backend systems, generating audit trails in specific formats, or redacting sensitive data at the protocol layer. These requirements are shaped by internal governance standards, industry regulations, and the specifics of each production environment. This post shows you how to deploy a serverless MCP proxy on Amazon Bedrock AgentCore Runtime that gives you a programmable layer to implement these controls. Amazon Bedrock AgentCore Gateway provides centralized governance and control for agent-tool integration, including…

Building AI-ready data: Vanguard’s Virtual Analyst journey

Artificial Intelligence Building AI-ready data: Vanguard’s Virtual Analyst journey Vanguard is a global investment management firm, offering a broad selection of investments, advice, retirement services, and insights to individual investors, institutions, and financial professionals. We operate under a unique, investor-owned structure and adhere to a straightforward purpose: To take a stand for all investors, to treat them fairly, and to give them the best chance for investing success. When Vanguard’s financial analysts needed to query complex datasets, they faced a frustrating reality: even basic questions required writing intricate SQL queries and sometimes long response times from data teams. This challenge is not unique to Vanguard: conversational AI is a scalable solution, providing analysts immediate responses. However, deploying conversational AI requires more than choosing the right foundation model—it requires AI-ready data infrastructure. In this post, you’ll learn how Vanguard built their…

Organizing Agents’ memory at scale: Namespace design patterns in AgentCore Memory

Artificial Intelligence Organizing Agents’ memory at scale: Namespace design patterns in AgentCore Memory When building AI agents, developers struggle with organizing memory across sessions, which leads to irrelevant context retrieval and security vulnerabilities. AI agents that remember context across sessions need more than only storage. They need organized, retrievable, and secure memory. In Amazon Bedrock AgentCore Memory, namespaces determine how long-term memory records are organized, retrieved, and who can access them. Getting the namespace design right is essential to building an effective memory system. In this post, you will learn how to design namespace hierarchies, choose the right retrieval patterns, and implement AWS Identity and Access Management (IAM)-based access control for AgentCore Memory. If you’re new to AgentCore Memory, we recommend reading our introductory blog post first: Amazon Bedrock AgentCore Memory: Building context-aware agents. What are namespaces? Namespaces are hierarchical…

Extracting contract insights with PwC’s AI-driven annotation on AWS

Artificial Intelligence Extracting contract insights with PwC’s AI-driven annotation on AWS This post was co-written with Yash Munsadwala, Adam Hood, Justin Guse, and Hector Hernandez from PwC. Contract analysis often consumes significant time for legal, compliance, and procurement teams, especially when important insights are buried in lengthy, unstructured agreements. As contract volumes grow, finding specific clauses and assessing extracted terms can become increasingly difficult to scale. Today, many teams rely primarily on keyword and pattern-based extraction or contract management systems to analyze contracts. While these methods can work, they often fall short of providing consistent insights at a scale. As a result, many teams are exploring AI-based approaches that can combine large language models (LLMs) with automated extraction workflows. PwC’s AI-driven annotation (AIDA) solution, built on AWS, can extract structured insights from contracts through rule-based extraction and natural language queries.…

NVIDIA Nemotron 3 Nano Omni model now available on Amazon SageMaker JumpStart

Artificial Intelligence NVIDIA Nemotron 3 Nano Omni model now available on Amazon SageMaker JumpStart Today, we are excited to announce the day zero availability of NVIDIA Nemotron 3 Nano Omni on Amazon SageMaker JumpStart. This multimodal model from NVIDIA combines video, audio, image, and text understanding into a single, efficient architecture, enabling enterprise customers to build intelligent applications that can see, hear, and reason across modalities in one inference pass. In this post, we walk through the model architecture and key capabilities of Nemotron 3 Nano Omni, explore the enterprise use cases it unlocks, and show you how to deploy and run inference using Amazon SageMaker JumpStart. Overview of NVIDIA Nemotron 3 Nano Omni NVIDIA Nemotron 3 Nano Omni is an open, multimodal large language model with 30 billion total parameters and 3 billion active parameters (30B A3B). It is…

Migrating a text agent to a voice assistant with Amazon Nova 2 Sonic

Artificial Intelligence Migrating a text agent to a voice assistant with Amazon Nova 2 Sonic Migrating a text agent to a voice assistant is increasingly important because users expect faster, more natural interactions. Instead of typing, customers want to speak and understand in real time. Industries like finance, healthcare, education, social media, and retail are exploring solutions with Amazon Nova 2 Sonic to enable natural, real-time speech interactions at scale. In this post, we explore what it takes to migrate a traditional text agent into a conversational voice assistant using Amazon Nova 2 Sonic. We compare text and voice agent requirements, highlight design priorities for different use cases, break down agent architecture, and address common concerns like tools and sub-agents for reuse and system prompt adaptation. This post helps you navigate the migration process and avoid common pitfalls. You can…

How Popsa used Amazon Nova to inspire customers with personalised title suggestions

Artificial Intelligence How Popsa used Amazon Nova to inspire customers with personalised title suggestions This post was co-written with Bradley Grantham and Hugo Dugdale from Popsa. Popsa is a technology company that helps users rediscover and relive the meaningful memories hidden in their photo libraries. Available across more than 50 countries and 12 languages, we use design automation and AI to transform everyday photos into personal, shareable experiences, including beautifully printed Photo Books. In 2016, we released PrintAI, a pioneering algorithm to take complete control of creating a varied and interesting design from a user’s photos. Our customers could use the algorithm to create Photo Books that appeared professionally designed, in less than 5 minutes. A core philosophy of our business is that technology should do the heavy lifting for our users, so automation has always been an intrinsic part…

Build Strands Agents with SageMaker AI models and MLflow

Artificial Intelligence Build Strands Agents with SageMaker AI models and MLflow Enterprises building AI agents often require more than what managed foundation model (FM) services can provide. They need precise control over performance tuning, cost optimization at scale, compliance and data residency, model selection, and networking configurations that integrate with existing security architectures. Amazon SageMaker AI endpoints align with these requirements by giving organizations control over compute resources, scaling behavior, and infrastructure placement, while benefiting from the managed operational layer of AWS. These models that are deployed by SageMaker AI, can power AI agents, handle conversational workloads, and integrate with orchestration frameworks like the FMs that are available on Amazon Bedrock. The difference is that the organization retains architectural control over how and where inference happens. In this post, we demonstrate how to build AI agents using Strands Agents SDK…

Build and deploy an automatic sync solution for Amazon Bedrock Knowledge Bases

Artificial Intelligence Build and deploy an automatic sync solution for Amazon Bedrock Knowledge Bases With Amazon Bedrock Knowledge Bases, you can give foundation models (FMs) and agents contextual information from your organization’s private data sources to deliver more relevant, accurate, and customized responses. As the data grows, maintaining real-time synchronization between Amazon Simple Storage Service (Amazon S3) and your knowledge bases becomes critical for accurate, up-to-date responses.In this post, we explore how Deloitte used Amazon EKS and vCluster to transform their testing infrastructure. In this post, we explore an automated solution that detects S3 events and triggers ingestion jobs while respecting service quotas and providing comprehensive monitoring. This serverless solution uses an event-driven architecture to keep your knowledge base current without overwhelming the Amazon Bedrock APIs. The challenge Knowledge bases in Amazon Bedrock require manual synchronization whenever documents are added,…

Automate repetitive tasks with Amazon Quick Flows

Artificial Intelligence Automate repetitive tasks with Amazon Quick Flows Consider a typical Monday morning: you’re manually copying data from several different systems to create a weekly report, then formatting it for different stakeholders. This single task can consume several hours that could be spent on more strategic work. Multiply this across your team, and these repetitive tasks add up quickly. Amazon Quick Flows automates these tasks using AI workflows. With Quick Flows, you create intelligent workflows using natural language—no coding or machine learning (ML) expertise required. You describe what you want automated, and Quick Flows builds it for you. This post shows you how to build your first AI-powered workflow, starting with a financial analysis tool and progressing to an advanced employee onboarding automation. What is Amazon Quick Flows? Amazon Quick Flows is part of Amazon Quick, a collection of…

Building Workforce AI Agents with Visier and Amazon Quick

Artificial Intelligence Building Workforce AI Agents with Visier and Amazon Quick Employees across every function are expected to make faster, better-informed decisions, but the information that they need rarely lives in one place. Workforce intelligence (who is in your organization, how they are performing, and where the gaps are) is one of the most valuable signals an enterprise has, and platforms like Visier are purpose-built to surface it. However, that intelligence only reaches its full value when it’s connected to the internal policies, plans, and context that give it direction. That context also often lives somewhere else entirely. Amazon Quick is the Agentic AI workspace where that connection happens. It brings together enterprise knowledge, business intelligence, and workflow automation. Its intelligent agents retrieve information and reason across all of these layers simultaneously, interpreting live data alongside organizational context to produce…

Amazon Quick for marketing: From scattered data to strategic action

Artificial Intelligence Amazon Quick for marketing: From scattered data to strategic action Imagine the following scenario: You’re leading marketing campaigns, creating content, or driving demand generation. Your campaigns are scattered and your insights are buried. By the time you’ve pieced together what’s working, the moment to act has already passed. This isn’t a tools problem because you have plenty of those. It’s a connection problem. Your marketing systems and tools are disconnected, so you spend time moving data between systems instead of improving campaigns or sharing results with your team. Amazon Quick changes how you work. You can set it up in minutes and by the end of the day, you will wonder how you ever worked without it. Quick connects with your applications, tools, and data, creating a personal knowledge graph that learns your priorities, preferences, and network. It…

Applying multimodal biological foundation models across therapeutics and patient care

Artificial Intelligence Applying multimodal biological foundation models across therapeutics and patient care Healthcare and life sciences decision making increasingly relies on multimodal data to diagnose diseases, prescribe medicine and predict treatment outcomes, develop and optimize innovative therapies accurately. Traditional approaches analyze fragmented data, such as ‘omics for drug discovery, medical images for diagnostics, clinical trial reports for validation, and electronic health records (EHR) for patient treatment. As a result, decision makers (CxOs, VPs, Directors) often miss critical insights hidden in the relationships between data types. Recent advancements in AI enable you to integrate and analyze these fragmented data streams efficiently to support a more complete understanding of therapeutics and patient care. AWS provides a unified environment for multimodal biological foundation models (BioFMs), enabling you to make more confident, timely decision-making in personalized medicine. This AI system combines biological data, model…

Cost-effective multilingual audio transcription at scale with Parakeet-TDT and AWS Batch

Artificial Intelligence Cost-effective multilingual audio transcription at scale with Parakeet-TDT and AWS Batch Many organizations are archiving large media libraries, analyzing contact center recordings, preparing training data for AI, or processing on-demand video for subtitles. When data volumes grow significantly, managed automatic speech recognition (ASR) service costs can quickly become the primary constraint on scalability. To address this cost-scalability challenge, we use the NVIDIA Parakeet-TDT-0.6B-v3 model, deployed through AWS Batch on GPU-accelerated instances. Parakeet-TDT’s Token-and-Duration Transducer architecture simultaneously predicts text tokens and their duration to intelligently skip silence and redundant processing. This helps achieve inference speeds orders of magnitude faster than real-time. By paying only for brief bursts of compute rather than the full length of your audio, you can transcribe at scale for fractions of a cent per hour of audio based on the benchmarks described in this post.…

Amazon SageMaker AI now supports optimized generative AI inference recommendations

Artificial Intelligence Amazon SageMaker AI now supports optimized generative AI inference recommendations Organizations are racing to deploy generative AI models into production to power intelligent assistants, code generation tools, content engines, and customer-facing applications. But deploying these models to production remains a weeks-long process of navigating GPU configurations, optimization techniques, and manual benchmarking, delaying the value these models are built to deliver. Today, Amazon SageMaker AI supports optimized generative AI inference recommendations. By delivering validated, optimal deployment configurations with performance metrics, Amazon SageMaker AI keeps your model developers focused on building accurate models, not managing infrastructure. We evaluated several benchmarking tools and chose NVIDIA AIPerf, a modular component of NVIDIA Dynamo, because it exposes detailed, consistent metrics and supports diverse workloads out of the box. Its CLI, concurrency controls, and dataset options give us the flexibility to iterate quickly and…

Get to your first working agent in minutes: Announcing new features in Amazon Bedrock AgentCore

Artificial Intelligence Get to your first working agent in minutes: Announcing new features in Amazon Bedrock AgentCore Getting an agent running has always meant solving a long list of infrastructure problems before you can test whether the agent itself is any good. You wire up frameworks, storage, authentication, and deployment pipelines, and by the time your agent handles its first real task, you’ve spent days on infrastructure instead of agent logic. We built AgentCore from the ground up to help developers focus on building agent logic instead of backend plumbing, working with frameworks and models they already use, including LangGraph, LlamaIndex, CrewAI, Strands Agents, and more. Today, we’re introducing new capabilities that further streamline the agent building experience, removing the infrastructure barriers that slow teams down at every stage of agent development from the first prototype through production deployment. Go…

Company-wise memory in Amazon Bedrock with Amazon Neptune and Mem0

Artificial Intelligence Company-wise memory in Amazon Bedrock with Amazon Neptune and Mem0 This post is cowritten by Shawn Tsai from TrendMicro. Delivering relevant, context-aware responses is important for customer satisfaction. For enterprise-grade AI chatbots, understanding not only the current query but also the organizational context behind it is key. Company-wise memory in Amazon Bedrock, powered by Amazon Neptune and Mem0, provides AI agents with persistent, company-specific context—enabling them to learn, adapt, and respond intelligently across multiple interactions. TrendMicro, one of the largest antivirus software companies in the world, developed the Trend’s Companion chatbot, so their customers can explore information through natural, conversational interactions (learn more). TrendMicro aimed to enhance its AI chatbot service to deliver personalized, context-aware support for enterprise customers. The chatbot needed to retain conversation history for continuity, reference company-specific knowledge at scale, and ensure that memory remained…

From developer desks to the whole organization: Running Claude Cowork in Amazon Bedrock

Artificial Intelligence From developer desks to the whole organization: Running Claude Cowork in Amazon Bedrock Today, we’re excited to announce Claude Cowork in Amazon Bedrock. You can now run Cowork and Claude Code Desktop through Amazon Bedrock, directly or using an LLM gateway. From startups to global enterprises across every industry, organizations build with Claude Code in Amazon Bedrock to boost developer productivity and accelerate delivery. With Amazon Bedrock you can build within your existing AWS environment, maintain enterprise security and regional data residency, and scale inference. Your data stays under your account’s controls: Amazon Bedrock does not store prompts, files, tool inputs and outputs, or model responses, and does not use them to train foundation models. With Claude Cowork in Amazon Bedrock, you can expand AI adoption to every knowledge worker in your organization, with a desktop application that…

End-to-end lineage with DVC and Amazon SageMaker AI MLflow apps

Artificial Intelligence End-to-end lineage with DVC and Amazon SageMaker AI MLflow apps Production machine learning (ML) teams struggle to trace the full lineage of a model through the data and the code that trained it, the exact dataset version it consumed, and the experiment metrics that justified its deployment. Without this traceability, questions like “which data trained the model currently in production?” or “can we reproduce the model we deployed six months ago?” become multi-day investigations through scattered logs, notebooks, and Amazon Simple Storage Service (Amazon S3) buckets. This gap is especially acute in regulated industries. For example, healthcare, financial services, autonomous vehicles, where audit requirements demand that you link deployed models to their precise training data, and where individual records might need to be excluded from future training on request. In this post, we show how to combine three…

Omnichannel ordering with Amazon Bedrock AgentCore and Amazon Nova 2 Sonic

Artificial Intelligence Omnichannel ordering with Amazon Bedrock AgentCore and Amazon Nova 2 Sonic Introduction Building a voice-enabled ordering system that works across mobile apps, websites, and voice interfaces (an omnichannel approach) presents real challenges. You need to process bidirectional audio streams, maintain conversation context across multiple turns, integrate backend services without tight coupling, and scale to handle peak traffic. In this post, we’ll show you how to build a complete omnichannel ordering system using Amazon Bedrock AgentCore, an agentic platform, to build, deploy, and operate highly effective AI agents securely at scale using any framework and foundation model and Amazon Nova 2 Sonic. You’ll deploy infrastructure that handles authentication, processes orders, and provides location-based recommendations. The system uses managed services that scale automatically, reducing the operational overhead of building voice AI applications. By the end, you’ll have a working system…

ToolSimulator: scalable tool testing for AI agents

Artificial Intelligence ToolSimulator: scalable tool testing for AI agents You can use ToolSimulator, an LLM-powered tool simulation framework within Strands Evals, to thoroughly and safely test AI agents that rely on external tools, at scale. Instead of risking live API calls that expose personally identifiable information (PII), trigger unintended actions, or settling for static mocks that break with multi-turn workflows, you can use ToolSimulator’s large language model (LLM)-powered simulations to validate your agents. Available today as part of the Strands Evals Software Development Kit (SDK), ToolSimulator helps you catch integration bugs early, test edge cases comprehensively, and ship production-ready agents with confidence. Prerequisites Before you begin, make sure that you have the following: - Python 3.10 or later installed in your environment - Strands Evals SDK installed: pip install strands-evals - Basic familiarity with Python, including decorators and type hints…

Accelerate Generative AI Inference on Amazon SageMaker AI with G7e Instances

Artificial Intelligence Accelerate Generative AI Inference on Amazon SageMaker AI with G7e Instances As the demand for generative AI continues to grow, developers and enterprises seek more flexible, cost-effective, and powerful accelerators to meet their needs. Today, we are thrilled to announce the availability of G7e instances powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs on Amazon SageMaker AI. You can provision nodes with 1, 2, 4, and 8 RTX PRO 6000 GPU instances, with each GPU providing 96 GB of GDDR7 memory. This launch provides the capability to use a single-node GPU, G7e.2xlarge instance to host powerful open source foundation models (FMs) like GPT-OSS-120B, Nemotron-3-Super-120B-A12B (NVFP4 variant), and Qwen3.5-35B-A3B, offering organizations a cost-effective and high-performing option. This makes it well suited for those looking to improve costs while maintaining high performance for inference workloads. The key highlights…

From hours to minutes: How Agentic AI gave marketers time back for what matters

Artificial Intelligence From hours to minutes: How Agentic AI gave marketers time back for what matters Your marketing team loses hours to page assembly, coordination emails, and review cycles. These manual workflows keep teams from their most important work: identifying what problems customers face, crafting messages that resonate, and building campaigns that drive meaningful engagement. In this post, we share how AWS Marketing’s Technology, AI, and Analytics (TAA) team worked with Gradial to build an agentic AI solution on Amazon Bedrock for accelerating content publishing workflows. The solution reduced webpage assembly time from up to four hours to approximately ten minutes (a reduction of over 95%) while maintaining quality standards across enterprise content management systems (CMS). Our marketing teams can now publish content faster and more consistently, freeing them to focus on finding more effective ways to reach and serve…

Introducing granular cost attribution for Amazon Bedrock

Artificial Intelligence Introducing granular cost attribution for Amazon Bedrock As AI inference grows into a significant share of cloud spend, understanding who and what are driving costs is essential for chargebacks, cost optimization, and financial planning. Today, we’re announcing granular cost attribution for Amazon Bedrock inference. Amazon Bedrock now automatically attributes inference costs to the IAM principal that made the call. An IAM principal can be an IAM user, a role assumed by an application, or a federated identity from a provider like Okta or Entra ID. Attribution flows to your AWS Billing and works across models, with no resources to manage and no changes to your existing workflows. With optional cost allocation tags, you can aggregate costs by team, project, or custom dimension in AWS Cost Explorer and AWS Cost and Usage Reports (CUR 2.0). In this post, we…

Optimize video semantic search intent with Amazon Nova Model Distillation on Amazon Bedrock

Artificial Intelligence Optimize video semantic search intent with Amazon Nova Model Distillation on Amazon Bedrock Optimizing models for video semantic search requires balancing accuracy, cost, and latency. Faster, smaller models lack routing intelligence, while larger, accurate models add significant latency overhead. In Part 1 of this series, we showed how to build a multimodal video semantic search system on AWS with intelligent intent routing using the Anthropic Claude Haiku model in Amazon Bedrock. While the Haiku model delivers strong accuracy for user search intent, it increases end-to-end search time to 2-4 seconds. This contributes to 75% of the overall latency. Now consider what happens as the routing logic grows more complex. Enterprise metadata can be far more complex than the five attributes in our example (title, caption, people, genre, and timestamp). Customers may factor in camera angles, mood and sentiment,…

Power video semantic search with Amazon Nova Multimodal Embeddings

Artificial Intelligence Power video semantic search with Amazon Nova Multimodal Embeddings Video semantic search is unlocking new value across industries. The demand for video-first experiences is reshaping how organizations deliver content, and customers expect fast, accurate access to specific moments within video. For example, sports broadcasters need to surface the exact moment a player scored to deliver highlight clips to fans instantly. Studios need to find every scene featuring a specific actor across thousands of hours of archived content to create personalized trailers and promotional content. News organizations need to retrieve footage by mood, location, or event to publish breaking stories faster than competitors. The goal is the same: deliver video content to end users quickly, capture the moment, and monetize the experience. Video is naturally more complex than other modalities like text or image because it amalgamates multiple unstructured…

Nova Forge SDK series part 2: Practical guide to fine-tune Nova models using data mixing capabilities

Artificial Intelligence Nova Forge SDK series part 2: Practical guide to fine-tune Nova models using data mixing capabilities This hands-on guide walks through every step of fine-tuning an Amazon Nova model with the Amazon Nova Forge SDK, from data preparation to training with data mixing to evaluation, giving you a repeatable playbook you can adapt to your own use case. This is the second part in our Nova Forge SDK series, building on the SDK introduction and first part, which covered kicking off customization experiments. The focus of this post is data mixing: the technique that lets you fine-tune on domain-specific data without sacrificing a model’s general capabilities. In the previous post, we made the case for why this matters, blending customer data with Amazon-curated datasets preserved near-baseline Massive Multitask Language Understanding (MMLU) scores while delivering a 12-point F1 improvement…

Cost-efficient custom text-to-SQL using Amazon Nova Micro and Amazon Bedrock on-demand inference

Artificial Intelligence Cost-efficient custom text-to-SQL using Amazon Nova Micro and Amazon Bedrock on-demand inference Text-to-SQL generation remains a persistent challenge in enterprise AI applications, particularly when working with custom SQL dialects or domain-specific database schemas. While foundation models (FMs) demonstrate strong performance on standard SQL, achieving production-grade accuracy for specialized dialects requires fine-tuning. However, fine-tuning introduces an operational trade-off: hosting custom models on persistent infrastructure incurs continuous costs, even during periods of zero utilization. The on-demand inference of Amazon Bedrock with fine-tuned Amazon Nova Micro models offers an alternative. By combining the efficiency of LoRA (Low-Rank Adaptation) fine-tuning with serverless and pay-per-token inference, organizations can achieve custom text-to-SQL capabilities without the overhead cost incurred by persistent model hosting. Despite the additional inference time overhead of applying LoRA adapters, testing demonstrated latency suitable for interactive text-to-SQL applications, with costs scaling by…

Transform retail with AWS generative AI services

Artificial Intelligence Transform retail with AWS generative AI services Online retailers face a persistent challenge: shoppers struggle to determine the fit and look when ordering online, leading to increased returns and decreased purchase confidence. The cost? Lost revenue, operational overhead, and customer frustration. Meanwhile, consumers increasingly expect immersive, interactive shopping experiences that bridge the gap between online and in-store retail. Retailers implementing virtual try-on technology can improve purchase confidence and reduce return rates, translating directly to improved profitability and customer satisfaction. This post demonstrates how to build a virtual try-on and recommendation solution on AWS using Amazon Nova Canvas, Amazon Rekognition and Amazon OpenSearch Serverless. Whether you’re an AWS Partner developing retail solutions or a retailer exploring generative AI transformation, you’ll learn the architecture, implementation approach, and key considerations for deploying this solution. You can find the code base to…

How Automated Reasoning checks in Amazon Bedrock transform generative AI compliance

Artificial Intelligence How Automated Reasoning checks in Amazon Bedrock transform generative AI compliance Compliance teams in regulated industries spend weeks on manual reviews, pay for outside consultants, and still face audit gaps when AI outputs lack formal proof. Automated Reasoning checks in Amazon Bedrock Guardrails address this by replacing probabilistic AI validation with mathematical verification, turning AI-generated decisions into provably correct, auditable results. In this post, you’ll learn why probabilistic AI validation falls short in regulated industries and how Automated Reasoning checks use formal verification to deliver mathematically proven results. You’ll also see how customers across six industries use this technology to produce formally verified, auditable AI outputs, and how to get started. The compliance challenge Regulated industries face high-stakes compliance challenges. Hospitals navigate radiation safety regulations. Financial institutions classify AI risk under the EU AI Act. Insurance carriers answer…