Category: Learning Levels

This blog highlights the approach Trellix took to accelerate the development and testing of product integrations and automated rules development. Using agentic AI methodology, Trellix is able to save over 40 hours of development time per integration resulting in a 90% reduction time to market. Trellix used Amazon Bedrock, LangChain, and Anthropic Claude in this effort.

Technology Innovation Institute (TII), an AWS Partner in the Middle East, advances privacy-preserving AI with their enhanced Federated Learning solution, PetalGuard. The solution enables organizations to collaborate on AI model development while maintaining strict data privacy and regulatory compliance. Learn how PetalGuard this development exemplifies TII’s position as a leading technology innovator in the Middle East and demonstrates the value of our strategic collaboration in delivering cutting-edge solutions for privacy-preserving AI development.

Artificial intelligence (AI) and Large Language Models (LLMs) are driving efficiency gains across the financial services and banking industry. Aisera’s AI Agent Platform, powered by AWS, is transforming financial services operations through domain-specific LLMs and AI agents. Learn how Aisera’s domain-specific LLMs and AI agents help customers achieve efficiency gains and improve customer service delivery through multi-agent collaboration.

Explore how Appian’s AI Copilot, powered by Amazon Bedrock is built directly into the Appian platform to assist both business users and developers in common tasks. For business users, AI CoPilot enables conversational data interaction through Records Chat feature, to help find patterns and trends from real-time data, and provides quick access to business specific documents through the Enterprise Copilot. Whereas, for developers, AI Copilot accelerates application development by assisting in requirements gathering to data modeling, UX design and object generation, while also automating test case creation and generating realistic sample data for testing.

Life sciences organizations invest significantly in real-world data (RWD), yet face persistent challenges in turning these investments into actionable insights. Panalgo’s IHD Cloud platform on AWS addresses these challenges by deploying advanced analytics capabilities directly within customers’ secure environments. Through this approach, organizations have reduced analysis time from months to days while maintaining strict data security and regulatory compliance standards.

AWS Marketplace announces AI Agents and Tools, offering hundreds of agent solutions including pre-built agents, agent tools, and professional services to help enterprises adopt and govern autonomous systems. Partners can now leverage new API-based and container-based delivery methods to package and scale their AI agent solutions directly to AWS customers.

Platform teams must balance rapid development with secure and compliant infrastructure in cloud computing environments. StackGen, an AWS Advanced Technology Partner, offers a solution that transforms manually created cloud environments into repeatable Infrastructure as Code while automatically enforcing security policies. The platform helps organizations reduce provisioning time by 75% while maintaining complete security coverage through automated policy enforcement and standardized templates.

Learn how Moneythor’s solution on AWS lowers customer acquisition cost and increases customer engagement through personalization and gamification in financial services by leveraging Amazon EKS, Amazon RDS and Amazon Bedrock.

At the 2025 Amazon Nova Partner Demo Competition, AWS Partners showcased innovative solutions using Amazon Nova foundation models to address customer challenges across industries. The competition highlighted Partners’ focus on Healthcare & Life Sciences and Financial Services sectors, leveraging Nova’s advanced AI capabilities.

Learn how to build scalable, real-time data lakes by combining Amazon S3 Tables with Snowflake integration. This solution enables efficient streaming data ingestion and analysis while automatically managing table optimization and maintenance. Discover how manufacturing companies can leverage this architecture to transform IoT sensor data into actionable insights, combining operational analytics with predictive maintenance capabilities. The integration between S3 Tables and Snowflake, along with SageMaker Lakehouse’s Iceberg REST endpoint, creates a robust foundation for streaming analytics that can handle high-volume data streams while maintaining performance and security.