Moderne is a developer collaboration platform that automates code refactoring and analysis, enabling organizations to migrate, maintain, and secure their source code at scale. The platform is designed to significantly accelerate code remediations, updates, and migrations—tasks that traditionally require months of manual work can now be completed in minutes.
Moderne addresses the challenges of keeping large, complex codebases up to date and secure. Its unique intellectual property allows users to accurately transform multiple code repositories at once, leveraging artificial intelligence (AI) to enhance code quality and safety. This mass-scale automation helps development teams reduce technical debt, improve security, and ensure code consistency across their software portfolio.
How Was Moderne Started?
Moderne was founded by Jonathan and Olga, both of whom bring extensive experience in software development and enterprise solutions. Jonathan previously founded OpenRewrite at Netflix and the Micrometer project as part of the Spring Team. With roles at Gradle and Pivotal, and as the author and co-author of technical books on automated code remediation and Java microservices, his expertise is central to Moderne's technology. Olga's background includes leading technical product management at Pivotal and engineering roles at Dell EMC, focusing on data protection and application delivery. Together, they co-authored "Automated Code Remediation: How to Refactor and Secure the Modern Software Supply Chain."
Who Uses Moderne?
Moderne primarily serves organizations that need to manage and modernize large-scale codebases efficiently, including enterprises in software, hospitality, insurance, and healthcare. Notable customers include:
- MEDHOST
- Interactions
- Allstate
- Choice Hotels
These companies rely on Moderne to streamline code migrations and secure their software supply chains.
What Makes Moderne Different?
Moderne differentiates itself through its ability to automate code transformations across multiple repositories simultaneously and to safely integrate AI-driven improvements. This approach minimizes manual intervention, reduces risk, and helps organizations maintain high standards of security and code quality at scale.
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