A Proven Approach to AI in mining operations
We help mining companies move from "we should do something with AI" to capturing measurable business value. Our approach is practical, collaborative, and designed to deliver results fast.
Talk to an ExpertWhat we do
AI doesn't start with technology, it starts with clarity.
We help industrial and mining companies move from "we should do something with AI" to "we're capturing real business value."
Where you are today determines how we begin.
Set the direction - identify and align
You see the potential of AI, but you're not sure where to start, or how to link it to real business goals, challenges, or opportunities.
That's where we begin.
We help you take a step back, connect the dots, and set a clear, confident direction.
Our process
Our approach is simple, structured, and proven, designed to uncover value fast and build leadership alignment from day one.
Leadership alignment & education
We start at the top.
A short, focused session with executives and VPs where we unpack what AI actually means for your business, cutting through the noise to show where it can drive real results.
You'll see examples from similar industries, understand what's possible with the data you already have, and align on what success should look like.
Leadership clarity and excitement, a shared vision of how AI can directly support business strategy.
Use case exploration
Next, we bring the business together.
Through structured, cross-functional workshops, we map out real operational challenges, inefficiencies, and opportunities, surfacing dozens of ideas that can be transformed into measurable value.
We score these ideas by business impact and data readiness, identifying the ones that are both technically feasible and financially meaningful.
A longlist of opportunities, from operational improvements to knowledge automation, ready for validation.
Data feasibility & prioritization
Finally, we test reality.
We validate the data behind the most promising ideas to confirm what's possible in the short term, and what needs groundwork.
We review systems, data sources, and dependencies (ERP, historian, maintenance logs, documents, etc.) and refine the shortlist to 2–3 high-ROI use cases ready for a Proof of Concept.
A practical, prioritized roadmap for your first AI initiatives, with a clear go/no-go foundation for next steps.
Two paths - one outcome
Every company starts somewhere different. That's why this process can unfold in two complementary ways:
Company-wide perspective
For leadership teams that want to define where AI fits across the business. We explore how AI supports key goals, reliability, performance, and knowledge continuity, and identify use cases that strengthen decision-making and efficiency across functions.
Example focus areas:
- Predictive maintenance and reliability
- Reporting, forecasting, and financial automation
- Knowledge assistants for expertise capture
- Safety, compliance, and document automation
Operational or asset-level focus
For teams that want to start small and prove value fast. We zoom in on your operational "flow sheet", identifying where bottlenecks or inefficiencies exist, and asking: can AI fix that?
Example focus areas:
- Mill and plant optimization
- Fuel and tire consumption analytics
- Equipment health and predictive maintenance
- Permit, compliance, and safety automation
Outcome: Whether strategic or operational, you'll see exactly where AI can create measurable value using the data you already have.
You'll walk away with
Why it works
We've run this process across industries facing the same challenges, strong data, complex operations, and uncertainty about where to start.
It's fast, collaborative, and grounded in reality.
Because the goal isn't just to talk about AI, it's to make your next step obvious, low-risk, and high-impact.
Prove the concept - launch a focused pilot
Once the direction is clear, the next step is to prove the value.
This is where ideas turn into evidence, where we test if AI truly delivers measurable business impact before scaling.
We call it a Proof of Concept, a focused pilot designed to be small, fast, and low-risk, but rich in learnings.
Because real confidence in AI comes from proof, not promises.
Our philosophy for operational AI projects
Low risk, rapid learning, and measurable business value, built on proven results across the mining industry
Our philosophy when it comes to AI projects
What is a PoC?
Similar to how miners test a new ore body before full-scale investment, a Proof of Concept (PoC) is how we validate the value of an AI idea – safely, quickly, and with measurable outcomes.
Purpose
- Validate technical feasibility before implementation
- Demonstrate measurable business value
- Test in a safe, controlled environment
The 4 core of a PoC
Why most mining companies are well-positioned for AI PoCs
Strong data foundation
Mining companies already capture vast amounts of valuable data, from process control systems and condition monitoring to maintenance logs and geological models. While data governance structures are still evolving, the foundation for predictive and operational AI use cases is already in place.
High-ROI use cases with proven feasibility
Across the sector, early machine learning initiatives are being driven by motivated individuals in areas like mill throughput prediction, equipment reliability, and document intelligence. By connecting these efforts under a common framework, companies can convert isolated experiments into scalable, high-ROI pilots.
Organizational readiness
The industry's hands-on, problem-solving mindset naturally supports fast, low-risk pilots that focus on measurable value. Combined with growing leadership interest in AI and an increasing shift toward data-driven decision-making, this creates strong readiness for operational AI projects that deliver real business outcomes.
Three proof-of-concepts we run in mining
The philosophy stays the same, but the focus depends on where AI creates the most value in your operation. Whether it's about improving equipment reliability, empowering your people, or optimizing plant throughput, each proof-of-concept is designed to deliver measurable value fast.
Predictive Maintenance for Mobile Equipment
Detect early signs of component failure using oil sample analysis, telemetry, and maintenance logs
How we solve it
We combine oil sample data, telemetry signals, and maintenance logs to predict component wear before failure occurs. Starting small, with one equipment type like haul trucks or shovels, we validate the model and prove ROI before scaling fleet-wide.
High-level project plan
| Phase | Description | Key Deliverables | Duration |
|---|---|---|---|
| 1. Scoping & Data Audit | Identify available data sources (oil samples, telemetry, maintenance logs). Define target failure modes. | Data inventory, success metrics | 2–3 weeks |
| 2. Data Integration & Cleaning | Merge datasets from lab systems, telemetry feeds, and CMMS. Ensure data quality and labeling. | Clean, labeled dataset | 3–4 weeks |
| 3. Model Development & Validation | Train predictive models on historical failures. Validate using accuracy and precision metrics. | Prototype model | 4 weeks |
| 4. Pilot & Live Testing | Deploy model for a small fleet. Compare predictions to inspection results. | Pilot dashboard | 4 weeks |
| 5. Review & Scale Decision | Evaluate ROI and accuracy. Recommend next fleet or system for rollout. | Impact report | 2 weeks |
You'll walk away with: A tested model that detects early failure risks, proven with your data, and ready to scale across assets.
Secure Enterprise GPT Assistant
Empower teams with an internal AI assistant that improves decision-making, productivity, and compliance
How we solve it
We deploy a private GPT environment trained on your internal documents, safety manuals, HR policies, maintenance reports, to create a secure assistant that reduces admin work and boosts accuracy. A live pilot with real users validates adoption and measurable time savings.
High-level project plan
| Phase | Description | Key Deliverables | Duration |
|---|---|---|---|
| 1. Foundation & Environment Setup | Deploy secure GPT instance with full access controls and audit logging. | Secure environment | 2 weeks |
| 2. Knowledge Curation | Gather and clean internal documents for ingestion. | Curated dataset | 3 weeks |
| 3. Assistant Training & Customization | Fine-tune retrieval and prompts for your teams. | Custom GPT assistant | 3 weeks |
| 4. Live Pilot | Run pilot with selected teams (HR, HSE, Finance). Gather feedback and monitor results. | Pilot report | 4 weeks |
| 5. Impact Measurement | Measure adoption, time saved, and user trust. | ROI and scale roadmap | 2 weeks |
You'll walk away with: A proven, secure AI assistant that reduces admin time, and a clear path to scale responsibly.
Plant Flow Optimization
Increase throughput and stability by identifying optimal operating ranges using process data
How we solve it
We use mill and plant process data, feed rate, density, power draw, to build a model that finds the best combination of variables for steady, efficient throughput. The model runs in "shadow mode" first, validating against historical data before live testing.
High-level project plan
| Phase | Description | Key Deliverables | Duration |
|---|---|---|---|
| 1. Problem Framing & Data Scoping | Define KPIs and collect historian data. Identify data gaps. | Process map, KPI baseline | 2 weeks |
| 2. Data Extraction & Feature Engineering | Build structured datasets with key operating variables. | Feature dataset | 3 weeks |
| 3. Model Building | Train regression or ML models to predict throughput and stability. | Optimization model | 4 weeks |
| 4. Validation & Shadow Testing | Run model on historical data to confirm performance. | Validation report | 3 weeks |
| 5. Operational Trial | Pilot insights in the control room for operator feedback. | Pilot report | 3 weeks |
You'll walk away with: A validated AI model that recommends process adjustments to boost throughput, built from your existing plant data.
Common thread across all three
Use existing data
No new CapEx or sensors required
Deliver results fast
Within 8–12 weeks
Build a foundation
For scaling AI across operations
Why it works
No matter where you start, enterprise or operational, the philosophy is the same:
Start small. Learn fast. Scale what works.
It's how we turn curiosity into clarity, and clarity into measurable results.
Scale and embed, build an AI program
You've seen what works, now it's time to scale. We help you build the structures, governance, and capabilities to make AI a sustainable part of how your organization operates.
We help you:
Establish secure, enterprise-grade AI environments.
Develop training, governance, and adoption programs.
Scale proven use cases across functions and sites.
You'll walk away with:
A long-term AI capability, not just pilots, with clear governance, measurable impact, and responsible adoption.
Talk to an Expert
Let's discuss how we can help you move from strategy to proven results.
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