Mining Digital Transformation That Delivers Measurable Savings
Not in Theory.
In Actual Mines.
Most transformation advisors have never sat in a mine control room, read a Wenco report, or diagnosed why Pronto and SAP are telling two different stories. We have. BHP Billiton. First Quantum Minerals. Live AI models in active mining environments. That is the foundation we bring to every engagement.
Why It Matters
$10M
Savings delivered in 12 months
FIRST QUANTUM - ZAMBIA
20+
Years in senior advisory across mining environments
3 CONTINENTS
$30M
Annual benefits delivered at a single mine site
MINING OPERATIONS, SOUTHERN AFRICA
5
Live AI models built for mining, deployed in phases
PREDICTIVE · HAUL · GRADE
THE REAL PROBLEM
Why $4 Billion in Savings Is Harder Than It Looks.
You have had the reports. You have seen the roadmaps. The recommendations sit on drives, referenced in meetings, implemented in pieces. Three things keep getting in the way.
01
The gap is not strategy. You have strategy. The gap is execution and accountability. Someone who will stay until the savings are in the bank, not until the slide deck is signed off.
02
Mining Complexity Is Consistently Under-estimated
Every mineral type has a different primary KPI, a different cost driver, and a different failure mode. Diamond, copper, gold, iron ore: generic digital frameworks break when they meet the specific data reality of your operation. The model that works for one rarely works for another.
03
Data Is Running the Mine. But Nobody Trusts It
Wenco, SAP, CAT MineStar, Pronto: multiple systems, multiple truths. Decisions get made on gut feel because no one is confident the data is clean, integrated, or telling the same story.
“Digital transformations fail because of leadership and governance gaps: not technology gaps. That is the problem we solve.”
EVERY MINERAL IS DIFFERENT
We Know What Matters for Your Mineral.
The data challenge in a diamond mine is not the same as in a copper mine, a gold operation, or an iron ore pit. Different minerals, different KPIs, different failure modes. We build models and governance frameworks specific to your operation: not generic mining templates applied regardless of context.
DIAMOND
Botswana · Southern Africa
Primary KPI
Grade maintained · ROM integrity · Recovery rate
Typical Data Problem
Multiple ore bodies with high grade variation. Decisions on blend and recovery made without a trusted single source of truth across geological models and plant feed data.
Our Approach
Grade-aware predictive models. Custody data integration. Dilution dashboards. ROM grade prediction before ore reaches the plant.
COPPER
Zambia · DRC · Chile
Primary KPI
Tonnes processed · Fleet cycle time · Energy cost per tonne
Typical Data Problem
Fragmented OT systems with poor fleet utilisation visibility. High volume operations where 1% cycle time improvement is worth millions but the data to find it does not exist in a usable form.
Our Approach
Fleet utilisation analytics. Haul road intelligence. Predictive maintenance across high-volume fleets. OT and ERP integration to build one source of truth.
GOLD
South Africa · Ghana · West Africa
Primary KPI
Grade control · Reagent consumption · Leach circuit recovery
Typical Data Problem
The gap between the geological model and what actually comes out of the plant. Reagent decisions made on schedule rather than real-time circuit data.
Our Approach
Grade control data integration. Leach circuit optimisation models. Reagent consumption analytics tied to actual recovery outcomes rather than fixed schedules.
IRON ORE
Southern Africa · West Africa
Primary KPI
Tonnes · Fe grade at ship · Rail and port throughput
Typical Data Problem
Operations optimise the pit but ignore the bottleneck downstream. Rail, port, and blending data live in separate systems with no connected view from pit to ship.
Our Approach
Full value chain integration from pit to ship. Blending optimisation for Fe grade targets. Rail and port logistics connected to mine production data.
AI THAT ACTUALLY WORKS
Five Models. Deployed in Phases.
Each Tied to a Savings Mechanism.
40% of AI projects will fail by 2027: not because the technology does not work, but because organisations automated broken processes. We only deploy AI when three conditions are met.
THE RULE
We only deploy AI when:
01
The data is clean
02
The process is documented and measurable
03
The ROI is signed off before build starts
LIVE
PHASE 1
Predictive Maintenance AI
Predictive failure detection across three warning windows
Identifies engines trending toward failure before they fail: giving maintenance teams time to plan rather than react. Deployed in phases as the data foundation is established.
SAVING: Eliminates unplanned engine failure. One unplanned failure on a major haul truck costs $500K to $2M in lost production and repair.
LIVE
PHASE 2
Oil Lab Classification AI
Four-state condition grading: Good, Fair, Watch, Action
Each oil sample graded automatically per compartment. Maintenance decisions triggered by data, not schedule or gut feel. Eliminates the guesswork that drives unnecessary costs.
SAVING: Proactive maintenance costs 3 to 5 times less than reactive. Across a major mining fleet, this is $10M to $30M annually.
PHASE 3
Grade Prediction Assist
Applicable across diamond, gold and copper operations
Predicts ore grade before it reaches the plant using available operational data. Grade-aware blend optimisation across operations.
SAVING: 1% improvement in grade management equals hundreds of millions in recovered value. The long-term AI opportunity in any grade-sensitive operation.
BUILT MODELS
PHASE 2
Haul Road Intelligence
Up to 60% of productivity tied to time management
AI upgrades the Haul Road Explorer into an intelligent system: flags which segments to prioritise based on predicted tonnes lost.
SAVING: 1% cycle time improvement across a major fleet equals millions annually. Compounds significantly at scale.
PHASE 2
Fleet Anomaly Detection
Early warning: deviation detected before failure occurs
Real-time alerts when a machine deviates from its baseline. Shift from reactive to predictive: from emergency shutdowns to planned maintenance windows.
Saving: Planned maintenance vs emergency shutdowns. Scheduled 8-hour stop vs 3-week production loss.
“We are not an AI company selling AI. We are implementers who use AI when it has earned the right to be used.”
THE APPROACH
We Deliver the Technical. Our Partner Delivers the People.
“Data without people who can use it changes nothing. The technical foundation and the human capability have to move together.”
Every phase has a fixed deliverables list, signed off before work begins. Any request outside the list triggers a formal change order with pricing. No change order, no work. This protects both parties.
WHERE WE START
The First 90 Days.
No strategy documents. No steering committee workshops. We go straight to the data.
DAYS 1–14
System Access and Stakeholder Map
Secure read access to all operational systems: Wenco, SAP, MineStar, Pronto
Map every report currently produced: who produces it, who reads it, and why
Interview 10 to 15 key decision makers on what decisions are made daily and on what data
Identify the three biggest data pain points cited by the operations team
We need to understand the current state before we change anything. Most failed transformations skip this step.
DAYS 15–45
Data Health Assessment
Full ETL extraction from all systems: what data exists, in what format, at what frequency
Cross-system reconciliation: where do the same KPIs give different numbers?
Identify data collected but never used (waste) and decisions made without data (risk)
Grade the data health of each system: Good, Fair, Watch, Action
You cannot reduce costs with bad data. Fixing the foundation creates immediate credibility.
DAYS 46–90
Opportunity Heat Map and Savings Pipeline
Build your specific Opportunity Heat Map: not a generic framework, your mine’s actual numbers
Prioritise top 5 use cases by implementation speed, savings potential, and data readiness
Present to leadership: here is where your savings are, with numbers attached
Agree Phase 2 deliverables list: signed, scoped, no surprises
By Day 90, you will have a savings roadmap with numbers attached. Not a strategy deck.
90
DAY 90 DELIVERABLE
A savings roadmap with your mine’s actual numbers, your top 5 prioritised opportunities, and a Phase 2 deliverables list ready to sign. No generic frameworks. No strategy decks. Your numbers.