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What Does It Really Mean to Scale AI in Industrial Controls?

Written by Connor Smith, Chief Product Officer | Apr 18, 2025 7:33:47 PM

Everyone’s talking about AI “at scale.” But for industrial operations, scaling AI isn’t about throwing GPUs at the cloud and hoping for insight. It’s about making intelligence practical, repeatable, and resilient — at the loop, system, and site level.

At Ai-OPs, we define scalable AI as:

  • Flexible enough to start small — on a single control loop.
  • Powerful enough to govern entire facilities.
  • Portable enough to move across industries and architectures.
  • Accessible enough for a control engineer, yet robust enough to support a full data science team.
  • Deployable anywhere — from an oil platform in the middle of the Gulf, to a super market in Hong Kong.

This is what we’ve built with Koios and Ronin.

A Scalable AI Platform: What It Should Be

Before diving into our tools, here’s what any truly scalable AI platform for industrial and commercial applications must do:

  1. Run Close to the Process

    • No AI should depend on cloud latency or internet access to control critical assets.

    • It should run on-prem, in your plant, facility, or building — fully air-gapped if needed.

  2. Speak the Language of Operations

    • Industrial protocols and capabilities like OPC-UA, Ethernet/IP, ModbusTCP, XML, Rest, SQL, or BACnet shouldn't be an afterthought.

    • Provide a robust system that provides high-availability, and redundancies built into the core functions, even for on-prem deployments.
    • Audit tracking and traceability 
    • The AI must plug into existing PLCs, DCSs, SCADA, and Datastores without vendor lock-in or end-to-end white glove services.

  3. Work for All Levels of AI Maturity

    • From someone experimenting with anomaly detection…

    • To a corporate data science team managing 100+ models and building deep reinforcement learning for closed-loop control.

  4. Scale Without Rebuilding Everything

    • A good AI model for one chiller, pump, or control loop should be replicable across others.

    • Think templated, reusable models — not handcrafted one-offs.

  5. Support Human Operators, Not Replace Them

    • Transparent. Traceable. Toggle-able.

    • Bumpless and safe transfers

    • AI should fit into the existing control room, not rip it out.

Koios: AI That Fits Everywhere

Koios is our self-hosted model inferencing platform — a lightweight, Docker-based AI engine built to deploy 1,000s of AI models in any industrial, commercial, or building automation environment.

How It Scales:

  • Single Loop? Replace PID with a model that learns from history and acts with foresight.
    Koios can drive the control output directly, with PID as backup, and bumpless transfer between them.

  • Multi-variable Supervisory Control? Koios can handle multiple inputs and outputs, pushing optimized setpoints to existing systems.

  • Multi-site Scalability? Deploy a Koios instance at every site — no specialized hardware or connectivity needed.

Where It Works:

  • Industrial Verticals: Oil & Gas, Mining, Chemical, Waste Water, Food & Beverage, Steel, Power Gen

  • Commercial Buildings: HVAC and chiller optimization, load balancing, IAQ control

  • Smart Infrastructure: Data centers, hospitals, universities, maritime, district energy

If it has data and a control system, Koios belongs there.

Ronin: Model Building for Everyone

On the other side of the equation is Ronin — our enterprise AI application that empowers users to build, train, and sustain machine learning and deep reinforcement learning models.

And here’s what makes it powerful:

From Enthusiast to Enterprise

  • Engineering Enthusiasts: Upload data, build your first anomaly detection, forecasting model, or deep reinforcement learning data— no coding required.

  • Control Engineers: Train deep reinforcement learning models that mimic your best operator — and deploy to Koios in a few click.

  • Data Science Teams: Bring your own algorithms, collaborate on sites and projects, maintain lifecycle control over dozens of models across assets.

Every Ronin user — whether it’s an R&D lab or a Fortune 100 refinery team — gets the same scalable, project-based interface. It’s AI as a tool, not AI as a black box.

Modular, Documented, Sustainable

  • All data transformations, training steps, and metadata are stored at the project level.

  • Templated models let you scale from one application to many, without retraining from scratch.

  • Support for supervised, semi-supervised, and DRL models gives you full flexibility — now and in the future.

Why This Matters

Many industrial AI platforms focus on one narrow use case or one narrow user. We didn’t.

We built Ai-OPs to scale horizontally across industries, and vertically across maturity levels

User Tool Application
Maintenance Tech Koios  Anomaly detection on a pump
Controls Engineer Ronin + Koios PID replacement using DRL
Data Science Team Ronin  System-wide forecast model
Energy Manager (Building) Koios Refrigeration & HVAC energy load balancing
R&D Lab Ronin + Koios Custom modeling for pilot scale plant
Facility Operator Ronin + Koios Optimize cooling tower usage

Final Thought: AI at Human Scale

Scaling AI doesn’t mean going big. It means going everywhere.

With Ai-OPs, AI can start at one loop and grow into an ecosystem — deployed locally, owned by the customer, and managed by the very teams who live in the control room day to day.

That’s the future of industrial AI: human-first, infrastructure-aware, and infinitely scalable.

🔧 Ready to explore? Let’s build your first model.
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