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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 […]

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

Google Images is turning 25. Here’s a look back at some major milestones — and new ways to explore and create visual content. ​ 

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.

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

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

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.

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

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

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