Machine Learning

Document processing in real estate is complex and highly manual, impacting critical business decisions at scale, making it ripe for automation. Built Technologies, a real estate finance software provider, processes over $500B in real estate projects. The company deployed an AI-powered document processing engine on Amazon Bedrock and the AWS Intelligent Document Processing (IDP) Accelerator. … Read more

The integration of AI into real-world applications has long been hindered by a fundamental challenge: the disconnect between systems that can see, systems that can think, and systems that can act. Developers have struggled with complex integrations, managing multiple APIs, and creating custom solutions to bridge these gaps, resulting in inefficient, costly, and often fragile … Read more

Using Amazon SageMaker Pipelines, organizations can automate their machine learning (ML) workloads and distribute them over many AWS accounts and AWS Regions as part of their Machine Learning Operations (MLOps) strategy. However, monitoring SageMaker Pipelines can become complex when they are distributed across many AWS environments. Developers and operations engineers must manually switch between multiple … Read more

Your prospects leave trails across multiple sources: a founder asks “What should I use for X?” in r/SaaS while their product launches on Hacker News. Stack Overflow questions spike. A GitHub repo crosses 2,400 stars. Each signal alone is noise, but correlated across sources, they reveal a prospect ready to buy. Multi-agent systems built with … Read more

Production quality assurance (QA) workflows require more than individual test execution. You must organize tests into regression suites that run as a batch, and integrate them into continuous integration and continuous delivery (CI/CD) pipelines so that test results gate deployments automatically. In a previous post, we introduced QA Studio, a reference solution for agentic QA … Read more

User experience (UX) testing faces multiple challenges that limit an organization’s ability to improve how users interact with their platforms. UX testing evaluates how easily and effectively users can navigate digital interfaces to complete intended tasks, such as finding products, creating accounts, or completing purchases. Unlike traditional Quality Assurance (QA) testing that focuses on functional … Read more

This post was written by Konstantin Lekh, Sasha Zinchuk, and Eugene Sergueev from Flo Health, and Liza (Elizaveta) Zinovyeva from AWS. In this post, we share how Flo Health’s engineering team turned a proof of concept (PoC) from the AWS Generative AI Innovation Center into a production-grade, AI-powered medical content review and generation system built … Read more

Healthcare organizations need efficient scheduling solutions, and ScienceSoft’s AI voice assistant, powered by Amazon Nova Sonic and Amazon Bedrock Guardrails, shows how responsible AI can deliver that. The AI patient scheduling software market is one of healthcare’s fastest-growing technology segments. According to Grand View Research, this market is growing rapidly, valued at approximately $260 million … Read more

Build with the smartest family of models from OpenAI yet, on Amazon Bedrock’s next-generation inference engine. Organizations scaling autonomous agents and AI-powered products need frontier intelligence that performs reliably across hundreds of steps, from coding agents shipping production code to cyber security research probing novel attack surfaces to genomics workflows analyzing entire gene sequences end-to-end. … Read more

AI as accessibility: what happened when a neurodivergent solutions architect stopped fighting his brain and started building. In this post, I share how AI serves as an accessibility tool for neurodivergent professionals. The system is built on Amazon Quick on your desktop, an AI-powered desktop and web assistant that compensates for executive function gaps every … Read more

This post is co-written with Vijay Venkatesh, CTO at Bluesight. If you build software for hospitals, you know that compliance work scales poorly. Hospitals managing 340B Drug Pricing Program compliance face a compounding data problem. Proving that a Group Purchasing Organization (GPO) purchased drug qualifies for an exception requires cross-referencing each purchase against several sources … Read more

When you deploy generative AI agents into multi-tenant production architectures, you face a specific identity problem: when an agent calls a downstream API on behalf of a user, whose identity travels with the call? Running the call as the agent’s service identity collapses the audit trail, because every downstream system must trust the agent unconditionally. … Read more

Deploying generative AI models to production requires finding the right combination of instance type, serving container with settings, and optimization strategy. This process typically requires a long iteration cycle of optimization and manual benchmarking. In April 2026, Amazon SageMaker AI launched this inference recommendations, so customers can programmatically get data-driven, production-ready configurations through APIs. This … Read more

Model customization transforms general-purpose AI models into specialized enterprise assets. By fine-tuning foundation models (FMs) on domain-specific data, businesses teach AI their unique workflows, terminology, and deep domain specialization, along with strict adherence to brand voice and fewer hallucinations. For enterprises, this is more than an optimization. It’s the creation of proprietary intellectual property. A … Read more

In dentistry, image quality determines whether a claim is paid or denied. Up to 20 percent insurance claims are initially denied, with missing or low-quality images among the leading causes. Yet quality assessment has traditionally been a manual, after-the-fact process. A clinician reviews an X-ray hours or days after capture, discovering problems only when a … Read more

In this post we show how to build a semantic layer on AWS using Stardog’s Semantic AI Application over Amazon Aurora and Amazon Redshift, and how to run a Strands Agents agent on Amazon Bedrock AgentCore that queries the layer to answer customer 360 questions across both sources without extract, transform, and load (ETL). The … Read more

An artificial intelligence (AI) agent can process an invoice, help adjudicate a claim, or classify a support ticket in a proof of concept. But running these agents across thousands or even millions of work items in a production environment introduces an entirely different set of challenges. At enterprise scale, success depends on much more than … Read more

This post was co-written with Daniel Han and Michael Han from Unsloth. Deploying large foundation models (FMs) stored at their original 16-bit floating-point precision (BF16 or FP16) is expensive. They need large GPU instances, driving up serving costs, and slowing down iteration cycles. Quantization addresses this by reducing the numerical precision of a model’s weights … Read more

When Model Context Protocol (MCP) tools underperform, the cause is rarely the protocol itself but the tool design. Many teams start by exposing an existing API as-is and trusting the agent to figure out the rest. It is a natural way to extend APIs to agentic systems and generative AI coding tools. For straightforward use … Read more

As enterprises scale their generative AI workloads, the demand for faster, more observable, and more flexible inference infrastructure continues to grow. Amazon SageMaker HyperPod is rising to meet that challenge with a set of new capabilities designed to streamline how organizations deploy and operate large models in production. Teams can now record inputs and outputs … Read more

Enterprises deploying Claude Code and Claude Desktop across development teams need centralized control over access, cost, and policy. At scale, this is hard to manage: each developer needs an individual credential, settings must be distributed manually, and spend is difficult to track or cap. Without a centralized control point, governance is left to whatever tooling … Read more

In pharmaceutical research, scientists face a fundamental challenge: accessing and connecting the vast amount of scientific knowledge scattered across disparate systems. From published literature and internal lab notes to genomics databases, critical insights remain trapped in silos, making it difficult for researchers to form comprehensive connections and generate promising hypotheses. This fragmentation slows down the … Read more

AI-powered email management can transform how organizations in the public sector handle constituent communications. By implementing intelligent email routing and prioritization systems, organizations can automatically classify and direct incoming messages based on urgency and departmental relevance. This technology is particularly useful in local government settings, where councillors receive diverse communications across multiple service areas. AI … Read more

When ecommerce teams need faster time-to-market for AI-powered customer experiences, they face weeks of custom integration work that delays launches and increases security risks. Building and connecting a production-ready AI assistant typically requires custom API code for each client, container infrastructure management, and complex authentication. Amazon Bedrock AgentCore and Mistral AI Studio streamline this process. … Read more

When you deploy generative AI agents with Amazon Bedrock AgentCore as production API endpoints, you might want to enforce web application firewall policies, rate limiting, protection against common web threats, or audit controls via AWS WAF. AWS WAF integrates with Elastic Load Balancing Application Load Balancers (ALBs), Amazon CloudFront distributions, and Amazon API Gateway REST … Read more

As organizations expand AI adoption across their workforce, IT administrators need a scalable way to manage how AI applications are configured and used on employee devices. These applications include Claude Code, Claude Desktop, and OpenAI Codex. Users, meanwhile, can open approved applications and start working without manual setup. Jamf, trusted by more than 78,000 organizations … Read more

If you’ve been managing Amazon Quick legacy Topics alongside your datasets, you know the challenge: two assets that must stay perfectly synchronized, each with its own permissions, lineage, and versioning. Column synonyms drift. Calculated fields diverge. A rename in the dataset breaks the Legacy Topic silently. You can now use Amazon Quick to embed that … Read more

Business intelligence analysts routinely face the same challenge at the start of every analytics project: the data needed to answer a single business question lives across multiple tables. Sales transactions sit in one place, customer demographics and product attributes in another, while returns, forecasts, and operational metrics occupy still others. Until now, combining these tables … Read more

In Part 1 of this series, we introduced Amazon Quick Sight Multi-Dataset Relationships and covered the foundational concepts of dimensional modeling, best practices for designing clean data models, and a decision framework for when to use runtime joins versus pre-joined datasets. If you haven’t read Part 1 yet, we recommend starting there. In this post, … Read more

Note: The topics referenced throughout this document refer to the new Topics experience (not legacy Topics). For details on the differences, see Build a unified semantic layer across datasets with multi-dataset Topics in Amazon Quick. Most real-world business questions span multiple tables. A retailer who wants to understand net revenue by product category must draw … Read more