Multi-Tenant Architecture and Flexible Data Model: A Foundation for PLM AI Agents

AI is taking industries, companies, and individuals by storm. While the interest in AI applications and technology is incredibly high, the actual application of AI technologies—and, most importantly, their business value across different industries—is still in development. At OpenBOM, we are researching and piloting use cases and applications of AI technologies and how they can be combined with unique value that OpenBOM platform brings to companies and industries.

Some time ago, we introduced the idea of BOM co-pilot, researching and experimenting with what’s possible when artificial intelligence meets product lifecycle management. As AI technology rapidly evolves, so does our ideas, experiments and visions. If you missed my earlier article check it here – End of SaaS PLM and AI Agents. The ideas of agentic architecture and examples of applications that can be programmed to do a work for you is very inspiring. 

Today, we want to share more ideas and research about how OpenBOM’s architecture makes it a platform for AI-driven PLM agents—and why this matters for the future of engineering, manufacturing, and supply chain intelligence.

Artificial intelligence in PLM isn’t just about adding chatbots or automating workflows. It’s about enabling a truly intelligent data foundation – a digital thread, one that understands relationships between product data, engineering decisions, procurement dependencies, and supply chain risks. For AI to work effectively in this environment, it requires a dynamic, scalable, and context-aware data model—something OpenBOM has been building from the beginning of the OpenBOM platform foundation. 

OpenBOM’s Scalable and Configurable Architecture

At its core, OpenBOM is an online service built on a multi-tenant, cloud-native foundation. Unlike traditional on-premise or hosted PLM or PDM systems that require heavy customization and/or complex deployment processes, OpenBOM is designed to run seamlessly on any cloud infrastructure—whether it’s AWS, Microsoft Azure, Google Cloud, or any hyperscaler. The multi-tenant nature provides a super easy way to start an account instantaneously. 

This scalability is key to enabling AI-driven workflows. AI applications require large amounts of structured and unstructured data, high levels of computational power, and the ability to process information in real-time. OpenBOM’s cloud-native infrastructure provides an elastic computing environment where AI agents can continuously analyze, learn, and optimize product and manufacturing data.

More importantly, OpenBOM’s multi-tenant architecture means that organizations don’t need to worry about infrastructure bottlenecks, outdated software versions, or costly maintenance. The system is always up to date, always scalable, and always ready to support new AI-driven capabilities as they emerge.

“PLM as Code” – Data Model as a Programmable System  

One of the biggest challenges in traditional PLM systems is the rigidity of data models and the complexity of their configuration. Traditional PLM solutions force companies to conform to predefined structures and complex setup process, making it difficult to adapt to new business models, supply chain configurations, or AI-driven insights.

OpenBOM takes a different approach—treating PLM as a code-driven system. This means that instead of relying on static, hardcoded data structures, OpenBOM allows companies to dynamically instantiate, modify, and extend their data models on the fly.

This flexibility is critical for AI agents, which need to interact with data in a way that is fluid and contextual rather than rigid and pre-defined. With OpenBOM, AI-driven applications can:

• Modify the PLM data model dynamically to accommodate new types of information, such as real-time IoT sensor data, external supplier intelligence, or generative design outputs.
• Automate data interactions by creating custom workflows that adjust based on AI-generated insights.
• Seamlessly integrate with external AI systems through OpenBOM’s open API and graph-based data architecture.

This “PLM as code” approach allows AI to go beyond simple automation and actively participate in data modeling, decision-making, and continuous optimization—a capability that traditional PLM systems simply don’t have.

Building a Product Knowledge Graph with Instant Data Import

Data is the foundation of AI, and the more structured and interconnected it is, the more powerful AI-driven insights can become. One of OpenBOM’s core capabilities is its ability to ingest any type of structured or semi-structured data and instantly transform it into a product knowledge graph. OpenBOM can ingest data from multiple data sources and 

Unlike traditional PLM databases that store information in isolated tables and records, OpenBOM’s graph-based data model allows AI agents to see relationships, dependencies, and patterns in product design, engineering changes, procurement decisions, and supply chain constraints.

For example, when a new component is introduced into OpenBOM, the system doesn’t just store it as an isolated item—it automatically connects it to its parent assemblies, supplier data, cost structures, compliance requirements, and historical usage patterns. This means AI-powered agents can immediately:

• Understand the full context of product data, rather than treating it as disconnected information.
• Identify risks and optimization opportunities, such as recommending alternative suppliers when one becomes unavailable.
• Analyze past engineering changes and suggest improvements, based on historical failure rates or supply chain trends.

This ability to instantly create a semantic understanding of product data is what makes OpenBOM a true AI-ready platform. Instead of requiring months of data preparation, AI applications can begin analyzing, learning, and acting on OpenBOM data immediately upon import.

Programming PLM AI Agents with OpenBOM 

The combination of OpenBOM’s multi-tenant architecture, flexible data model, and graph-based data relationships creates an ideal foundation for AI-driven PLM agents. These AI agents can do much more than just automate workflows—they can become active participants in engineering, manufacturing, and supply chain decision-making.

By leveraging large language models (LLMs) and other AI technologies, OpenBOM can be programmed to interact intelligently with users. AI agents can:

• Analyze and recommend design modifications based on real-time cost, availability, and performance data.
• Predict supply chain disruptions by monitoring global supplier networks and procurement trends.
• Assist engineers in real-time by understanding product configurations, compliance requirements, and engineering constraints.
• Automate complex decision-making processes, such as selecting the optimal supplier based on historical performance and lead times.

This isn’t just about automating tasks—it’s about augmenting human decision-making with AI-powered intelligence. With OpenBOM, AI doesn’t just sit on top of the system as an afterthought—it becomes an integral part of how product data is managed, analyzed, and utilized.

Conclusion: 

The AI-Powered Future of OpenBOM is Coming. AI is changing the way companies approach PLM, and OpenBOM is at the forefront of this transformation. OpenBOM multi-tenant architecture, programmable data model, and knowledge graph capabilities make OpenBOM the ideal platform for AI-powered PLM solutions.

We are excited about what’s coming next, and we’ll be sharing more soon about how OpenBOM is enabling AI-driven PLM agents to transform engineering, manufacturing, and supply chain intelligence.

If you’re interested in exploring how AI and OpenBOM can work together, or if you have ideas for AI-powered PLM applications, we’d love to hear from you.

We are looking for customers to discuss our ideas and planned features. Contact us to discuss more. And stay tuned… 

Best, Oleg