Artificial Intelligence
Category: Customer Solutions
How Palo Alto Networks enhanced device security infra log analysis with Amazon Bedrock
Palo Alto Networks’ Device Security team wanted to detect early warning signs of potential production issues to provide more time to SMEs to react to these emerging problems. They partnered with the AWS Generative AI Innovation Center (GenAIIC) to develop an automated log classification pipeline powered by Amazon Bedrock. In this post, we discuss how Amazon Bedrock, through Anthropic’ s Claude Haiku model, and Amazon Titan Text Embeddings work together to automatically classify and analyze log data. We explore how this automated pipeline detects critical issues, examine the solution architecture, and share implementation insights that have delivered measurable operational improvements.
From beginner to champion: A student’s journey through the AWS AI League ASEAN finals
The AWS AI League, launched by Amazon Web Services (AWS), expanded its reach to the Association of Southeast Asian Nations (ASEAN) last year, welcoming student participants from Singapore, Indonesia, Malaysia, Thailand, Vietnam, and the Philippines. In this blog post, you’ll hear directly from the AWS AI League champion, Blix D. Foryasen, as he shares his reflection on the challenges, breakthroughs, and key lessons discovered throughout the competition.
How the Amazon AMET Payments team accelerates test case generation with Strands Agents
In this post, we explain how we overcame the limitations of single-agent AI systems through a human-centric approach, implemented structured outputs to significantly reduce hallucinations and built a scalable solution now positioned for expansion across the AMET QA team and later across other QA teams in International Emerging Stores and Payments (IESP) Org.
How AutoScout24 built a Bot Factory to standardize AI agent development with Amazon Bedrock
In this post, we explore the architecture that AutoScout24 used to build their standardized AI development framework, enabling rapid deployment of secure and scalable AI agents.
How Omada Health scaled patient care by fine-tuning Llama models on Amazon SageMaker AI
This post is co-written with Sunaina Kavi, AI/ML Product Manager at Omada Health. Omada Health, a longtime innovator in virtual healthcare delivery, launched a new nutrition experience in 2025, featuring OmadaSpark, an AI agent trained with robust clinical input that delivers real-time motivational interviewing and nutrition education. It was built on AWS. OmadaSpark was designed […]
How Beekeeper by LumApps optimized user personalization with Amazon Bedrock
Beekeeper’s automated leaderboard approach and human feedback loop system for dynamic LLM and prompt pair selection addresses the key challenges organizations face in navigating the rapidly evolving landscape of language models.
Sentiment Analysis with Text and Audio Using AWS Generative AI Services: Approaches, Challenges, and Solutions
This post, developed through a strategic scientific partnership between AWS and the Instituto de Ciência e Tecnologia Itaú (ICTi), P&D hub maintained by Itaú Unibanco, the largest private bank in Latin America, explores the technical aspects of sentiment analysis for both text and audio. We present experiments comparing multiple machine learning (ML) models and services, discuss the trade-offs and pitfalls of each approach, and highlight how AWS services can be orchestrated to build robust, end-to-end solutions. We also offer insights into potential future directions, including more advanced prompt engineering for large language models (LLMs) and expanding the scope of audio-based analysis to capture emotional cues that text data alone might miss.
Architecting TrueLook’s AI-powered construction safety system on Amazon SageMaker AI
This post provides a detailed architectural overview of how TrueLook built its AI-powered safety monitoring system using SageMaker AI, highlighting key technical decisions, pipeline design patterns, and MLOps best practices. You will gain valuable insights into designing scalable computer vision solutions on AWS, particularly around model training workflows, automated pipeline creation, and production deployment strategies for real-time inference.
Scaling medical content review at Flo Health using Amazon Bedrock (Part 1)
This two-part series explores Flo Health’s journey with generative AI for medical content verification. Part 1 examines our proof of concept (PoC), including the initial solution, capabilities, and early results. Part 2 covers focusing on scaling challenges and real-world implementation. Each article stands alone while collectively showing how AI transforms medical content management at scale.
How dLocal automated compliance reviews using Amazon Quick Automate
In this post, we share how dLocal worked closely with the AWS team to help shape the product roadmap, reinforce its role as an industry innovator, and set new benchmarks for operational excellence in the global fintech landscape.









