Why End Users Should Own Their Models, But Not the Infrastructure

By Ai-OPs, Inc. | https://ai-op.com

The Opportunity: Industrial AI Is Finally Delivering

The era of Industrial AI is here—and it’s changing how process control is done across sectors like energy, chemicals, mining, manufacturing, and water. Companies now have the tools to model, predict, and optimize complex operations using deep reinforcement learning (DRL), time series forecasting, and anomaly detection.

But one critical decision remains: Should you build your own models? And more importantly, should you build your own platform to do it?

At Ai-OPs, our answer is simple:

Yes to models. Absolutely not to platforms.

Build Your Own Models: The Path to Control and Customization

Industrial processes are complex, sensitive, and full of proprietary data. The only way to truly capture the nuance of your operation is to own your model development.

Hybrid Industrial AI often fails to meet the mark because it's not completely trained on your data, your process, or your constraints. Building your own models ensures:

• Process-specific accuracy
• IP retention and data sovereignty
• Deep integration with your operations team’s knowledge
• Scalable reuse via templated models (e.g., similar furnaces or tanks across multiple sites)

That’s why Ronin, our industrial-grade AI build environment, is designed for engineers—not data scientists alone. It gives industrial teams the ability to build, train, and sustain AI/ML models in a secure, compliant, and production-ready environment.

Don’t Build Your Own Platform: The Trap of One-Time Success

Some companies believe that if they can stand up an AI project once, they can do it forever. The truth is:

Just because you can make it work one time doesn’t mean you want to support it all the time.

AI platforms are not just apps. They require:

• Secure deployment and version control
• License and usage tracking
• Long-term development goals
• Workflow lifecycle management
• Multi-model orchestration and inference
• User access control and audit logs
• Edge compatibility and air-gapped options
• Constant upkeep for industrial protocols like OPC-UA, Ethernet/IP, REST, XML, Modbus, etc.

Trying to build and sustain all that internally leads to non-scalable, fragile solutions that break as soon as the engineer who wrote the first script leaves.

Instead, we built Koios—a pre-trained model deployment and management solution purpose-built for industrial environments. It runs on a single Docker container, doesn’t require internet access, and is built to be hardware-agnostic and fault-tolerant.

Standardization: The Key to Scaling AI Across Sites and Teams

What makes a successful AI rollout?

• Repeatability
• Maintainability
• Security
• Compliance
• Ease of handoff

Standardized platforms like Ronin (for building) and Koios (for deploying) make your AI journey repeatable and sustainable across many sites and users—even if your internal champions rotate out or retire.

Ai-OPs enables site-level segregation, organization-level access control, and full model traceability, so your AI doesn't just succeed once—it becomes a part of your everyday operations.

Your Infrastructure Partner Matters More Than Ever

As the AI ecosystem evolves, so does the open-source software that underpins innovation. From PyTorch and TensorFlow to Docker and protocol libraries, industrial teams depend on infrastructure that can adapt quickly, incorporate modern tooling, and stay compatible with upstream open-source advancements.

At Ai-OPs, we believe your infrastructure partner must move in step with the open-source community, not lag behind it. That’s why our tools are built on open standards, containerized deployment, and modular architectures. You shouldn’t be locked into stale, outdated tools in a fast-moving environment. Your platform should evolve as the tools evolve.

Why Ai-OPs Exists

Ai-OPs was founded by control engineers, for control engineers. We believe DRL is the most effective method for optimizing complex industrial systems, and we built the tools—Ronin and Koios—to bring that power into real-time control environments.

We didn’t stop at model accuracy—we solved for inference at the edge, lifecycle management, air-gapped deployments, and long-term sustainment.

Our mission isn’t to “do Industrial AI.”
Our mission is to make Industrial AI work—everywhere, for everyone.