Artificial Intelligence
Category: Customer Solutions
How OneAdvanced deployed over 50 AI agents on UK-sovereign AWS
Learn how OneAdvanced, a UK enterprise software provider, built a UK-sovereign AI platform by self-hosting Llama 4 Maverick and Llama Guard 4 on Amazon SageMaker AI, with a RAG pipeline on pgvector and over 50 agents built with Strands Agents SDK on Amazon ECS.
Pay with confidence: How Solv Labs built verifiable, auditable agent payments on Amazon Bedrock AgentCore payments
Solv Labs built a governed agent-payments workflow on Amazon Bedrock AgentCore payments, where every transaction is authorized, attested in an AWS Nitro Enclave, priced for risk, and anchored to a public blockchain before settlement. See how the pattern gives enterprises a verifiable, auditable trail for autonomous agent payments in regulated environments.
How ONESTRUCTION built the Ishigaki-IDS foundation model with AWS GenAIIC
ONESTRUCTION, with technical advisory from the AWS Generative AI Innovation Center, built Ishigaki-IDS, a foundation model specialized for construction and BIM workflows. This architectural case study shows how they combined synthetic data, a three-stage training pipeline, and verifiable rewards on Amazon EC2 to build a domain model in a data-scarce field.
How Pixieset achieved 35% AI feature adoption by solving the right problem with Amazon Bedrock
Photographers are among the most skeptical audiences for generative AI. Learn how Pixieset used Amazon Bedrock to launch an AI-generated alt text feature to millions of users in four months, reaching 35% adoption by automating the tedious image SEO work photographers avoid, without touching the creative craft they take pride in.
First Orion accelerates QA automation using Amazon Nova Act
Learn how First Orion, a branded communications company, shifted from brittle script-based UI testing to AI-driven QA automation with Amazon Nova Act. By describing tests in plain English instead of maintaining selector-based code, they cut QA cycle times, freed engineering capacity, and caught regressions earlier.
How nOps shipped FinOps agents 75% faster with Amazon Bedrock AgentCore
nOps rebuilt its Clara FinOps AI agent on Amazon Bedrock AgentCore, replacing a self-managed Amazon EKS stack running LangChain and LangGraph. The move cut time-to-production by 75% (from 10-12 months to 4 months), improved response quality, and reduced operational overhead while keeping analytics governed through Databricks Lakehouse Metric Views.
How Cohere Health digitizes clinical policies using Amazon Bedrock AgentCore
In this post, you learn how Cohere Health built a multi-tenant agentic architecture on AgentCore using AgentCore Runtime’s secure MicroVM isolation, unified tool access through AgentCore Gateway, AgentCore Memory, and the Agent Skills open standard to rapidly scale policy digitization capabilities, while preserving transparency, version control, and human oversight.
How LendingTree built a multi-agent mortgage assistant on Amazon Bedrock
Learn how LendingTree built a production multi-agent mortgage assistant on Amazon Bedrock. Three coordinated agents use LangGraph, the Model Context Protocol, and Amazon Nova models with built-in guardrails to deliver 24/7 personalized mortgage guidance while meeting strict financial-services compliance.
How Mobileye transformed support operations using Amazon Bedrock AgentCore
In this post, we’ll explore how Mobileye deployed an AI support agentic solution on Amazon Bedrock AgentCore – from the support bottleneck that sparked the idea, through the proof of concept that validated it, to the hybrid architecture that bridges on-premises systems with AWS cloud services. This approach is relevant for enterprises struggling to scale AI Agents while maintaining enterprise grade governance and security standards.
From weeks to minutes: How Formula 1® uses agentic AI on AWS to accelerate data operations
Formula 1® partnered with AWS to build the Data Accelerator, using agentic AI on Amazon Bedrock AgentCore to transform its MarTech data platform. Learn how F1 cut data source onboarding from up to 8 weeks to about 40 minutes, automated schema evolution, and gained end-to-end observability across its fan-engagement data estate.









