AI/ML Scalability at Samarco's OT

The story of AI/ML at Samarco didn’t begin with a massive data science team or an ambitious enterprise-wide program.

It began in 2022, when a handful of automation engineers developed a simple virtual sensor to estimate mass flow on a conveyor belt.

That model worked. It helped operators detect anomalies earlier, avoided blockages in crushers and screens, and kept the process flowing.

What seemed like a small step revealed a huge opportunity: if one conveyor could benefit from AI/ML, why not 30?

But scaling wasn’t just about copying code. It required a holistic approach around People, Processes, and Technology.

People: Building Internal Capability
 

One of the most remarkable aspects of this journey is that Samarco itself developed 13 of the 15 models currently in production.

This was only possible because the company created a dedicated Process Optimization team inside OT. Multidisciplinary by design, the team combined automation, process engineering, and data science skills. With Aignosi’s support, Samarco conducted an analytics maturity diagnosis to identify gaps, set priorities, and build internal skills.

This organizational move was crucial: instead of outsourcing model development, Samarco empowered its own people to own the models — designing, deploying, and maintaining them. AI became part of the operational DNA, not a black-box service.

Processes: Governance and Repeatability
 

The next step was to standardize. Without clear governance, models risk becoming isolated “one-off experiments.” Samarco adopted AIOps for OT, incorporating best practices from MLOps, DataOps, and DevOps:

• Version control for models and datasets
• Automated retraining pipelines
• Continuous monitoring for drift and anomalies
• End-to-end observability of the models in production.
• Standardized workflows and templates for rapid replication

  
  

  This turned model deployment into a repeatable, reliable process. What once took months could now be done in days.

   

These processes turned AI from a one-time success into a repeatable system, cutting deployment lead-time from months to days.

Technology: SIENTIA™ as the Enabler
 

The backbone of this transformation is SIENTIA™, an on-premise AIOps platform designed for OT environments. Unlike generic IT tools, it was built to meet industrial realities:

• Native OPC UA connectors for integration with SCADA, PLCs, and PIMS
• 100% on-premise deployment, addressing cybersecurity and latency requirements
• Real-time inferencing, ensuring AI insights are available when operations need them
• No-code templates, allowing engineers and operators to configure models without Python expertise
   

This meant that Samarco’s own team could focus on process knowledge, while SIENTIA™ took care of governance, automation, and observability.

 The Result

In less than 90 days, Samarco went from 1 model to 15 models in production — and 13 of those were developed internally by its own Process Optimization team.

This is more than scale. It’s organizational autonomy, where AI is not just a project but a capability owned by the operation itself.

And this is only the beginning. The roadmap now points to 50 models by the end of 2025, positioning Samarco as one of the leaders in applying Industrial AI to mining operations in Brazil.

We will bring more stories about how Samarco is continuing to scale their AI/ML in the shop floor.