RELEX Launches RELEX Open, Giving Customers Three Ways to Accelerate AI Adoption
May 7, 2026 • 3 minInnovative platform architecture lets customers deploy proven solutions, connect their own AI, and build more capabilities on a single foundation
RELEX Solutions today introduced RELEX Open, an innovative architecture for its AI-native platform that gives retailers, manufacturers, and wholesalers three clear ways to accelerate AI adoption: deploying proven planning capabilities at scale, connecting their own AI agents and systems to RELEX, and extending capabilities directly on the platform into new operational areas of their business.
“AI is reshaping how enterprise software gets built, bought, and used. The question is no longer build or buy. It’s how fast you can build on the right foundation,” said Mikko Kärkkäinen, Group CEO and Co-Founder, RELEX Solutions. “RELEX Open gives companies a proven foundation they can deploy immediately, with the ability to extend the platform to support the capabilities that differentiate their business without starting from scratch.”
RELEX Open builds on two decades of domain expertise and a shared data foundation shaped by more than 700 customers, so every capability starts from proven intelligence rather than a blank slate.
RELEX Open supports three distinct starting points:
- Deploy what’s proven. Forecasting, replenishment, merchandising, promotions, and manufacturing planning capabilities, already validated by more than 700 customers worldwide, configured to each business without custom development.
- Connect what you have. External AI agents and enterprise systems connect to RELEX through open protocols and Model Context Protocol (MCP), enabling real-time, bidirectional interaction. RELEX will both call external agents and be called by them, and can orchestrate workflows across systems or operate within orchestrations defined by other platforms, ensuring decisions and actions are aligned on a shared, consistent data foundation. This allows users to interact with RELEX through their preferred AI tools or bring RELEX data and decisions into their own AI environments.
- Extend what you can do on RELEX. Teams can build new capabilities on the RELEX platform to expand beyond traditional planning into adjacent, high-value use cases, using its extensibility framework to create data models, algorithms, workflows, and user experiences in hours rather than months.
This positions RELEX as part of a broader AI ecosystem, where agents and systems collaborate across organizational boundaries rather than operating in isolation. AI agents and systems can act on the same underlying data, workflows, and decision logic within RELEX, without needing to rebuild them for each new interface or use case.
All three approaches run on the same platform, and can be used together, depending on each organization’s needs, with governance and control built in. Every decision, whether made by an AI agent, a model, or a human planner, follows the same business rules and is fully traceable. Organizations define their level of automation, with the ability to review, adjust, or reverse decisions at any point.
“What separates platforms delivering real AI value from those still promising it is accumulated intelligence,” said Steve Rowen, Managing Partner, Retail Systems Research. “After hundreds of deployments, RELEX brings the kinds of embedded domain knowledge that organizations cannot replicate on their own. These are the types of solutions that make the required foundation for modernization more accessible, whether companies want to deploy, connect, or build.”
RELEX serves customers across retail, manufacturing, and wholesale globally. RELEX customers are already exploring early deployments of extensibility and AI connectivity in pilot environments. These initiatives focus on enabling faster innovation cycles and giving teams the ability to build and test new capabilities directly on the platform, without waiting for traditional release cycles.
Register here for the upcoming webinar, “Your AI strategy is moving faster than your platform,” to learn how to scale AI without adding complexity.