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When your document repository contains hundreds of millions of files accumulated over nearly a decade, how do you systematically find and redact sensitive customer data without taking years to complete? This was the challenge facing The Huntington National Bank (Huntington), a top 10 bank in the United States. Redacting sensitive information at scale Since 2015, […]

If you run a clinic or hospital network, you already know the cost of missed appointments. The average no-show rate across US healthcare sits between 5–30 percent, depending on specialty. Each empty slot represents lost revenue, idle provider time, and delayed patient care. The standard fix, calling patients one by one to confirm or reschedule,

One dashboard shows 42,000 active movie view counts while another shows 38,500. Your chat agent references a third number entirely. Data teams spend hours reconciling numbers instead of answering strategic questions, and trust in analytics erodes. This is a pattern that we see across many organizations. Teams spend more effort reconciling numbers than actually using

Loka transformed customer voice interactions by building a conversational AI agent with Amazon Nova 2 Sonic that keeps customers engaged with natural, responsive experiences. Their AWS-based solution achieves high speech reasoning accuracy on Big Bench Audio while delivering significantly lower costs and faster response times than traditional voice AI pipelines. In this post, we demonstrate

Protein researchers face a time-consuming challenge: manually searching through thousands of peptide sequences to find structurally similar candidates is slow, error-prone, and requires deep domain expertise to interpret results. Building a protein research copilot can transform how researchers search for structurally similar peptides across large datasets — enabling natural language queries, automated embedding generation, and

Building multi-tenant AI applications presents new architectural challenges. You need complete tenant isolation between customers, different service tiers with different capabilities, granular cost tracking, and observability per tenant. Without these, you could risk exposing customer data, not providing appropriate quality of service to your customers or running up unforeseen costs. In this post, you will

This post was co-written with Kevin Jones from Ampersend (Edge & Node) and Chethan Shriyan from the Amazon Bedrock AgentCore Payments team. Ampersend and Amazon Bedrock AgentCore Payments are addressing one of the hardest problems in agentic AI. How do autonomous agents pay for services without developers building bespoke billing integrations, credential management, and payment

Turning a library of aerial imagery into a natural-language-searchable knowledge base is a problem that touches every industry that relies on geospatial data — insurance, real estate, government, infrastructure, and agriculture. The traditional path requires either manual tile-by-tile inspection or training a bespoke computer vision model for each new question. Multimodal embeddings, large language model

With ComfyUI workflows on Amazon SageMaker AI processing jobs, you can automate content generation at scale. For enterprises, every delay or misstep in creating compelling multimedia assets can mean lost sales, faded brand relevance, or missed marketing deadlines. When a product launch deadline looms or a seasonal promotion needs urgent assets, waiting for designers to

AI agents are changing how organizations find and act on information, but they share one structural limitation: their knowledge is frozen at training time. When you ask an agent that relies only on its training data about today’s stock price, a sports score, or a release that shipped an hour ago, it can’t respond. Web