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Open-pit mine with haul trucks at dawn

Predictive maintenance · Sub-Saharan mining

An unplanned haul truck
stoppage costs  R90,000–R180,000
per hour.

TradeCore builds predictive maintenance capability on the sensor data your operation already records — on-premise, on your existing SCADA and historian infrastructure.

Book a Diagnostic Audit Request a technical discussion
2,000–20,000 tpd
Operations we work with
25–45%
Of operating cost is maintenance
0
New sensors required
On-premise
Deployment model
01 / The Position

The data is already
being collected.
Nothing is reading it.

Instrumented, not analysed

Most mid-cap operations already stream equipment sensor data through SCADA and a data historian — vibration, temperature, pressure, load, run hours. In practice that archive is used for post-incident review, not for anticipating the next failure. There is no analytical layer reading it continuously.

Maintenance carries the cost base

Maintenance typically represents 25–45% of total operating cost at a mining operation. A meaningful share of that is consumed by work that was scheduled too early, or by recovery from failures that gave warning in the data long before they stopped the fleet.

Prediction on existing infrastructure

TradeCore connects to the data infrastructure you already run and predicts developing failures before they reach shutdown. No new sensors to install, no cloud dependency, no disruption to control systems or maintenance planning already in place.

Maintenance engineer reviewing sensor data at an industrial control terminal

Condition data from fixed and mobile plant — read where it already lands.

02 / Engagement Model

Three stages, scoped in that order

01

Diagnostic Audit

A scoped assessment of your existing data infrastructure, tag coverage and equipment fleet. We establish where historian data is complete enough to support failure prediction, and where it is not. The output is a written readiness position and an expected-value estimate per asset class.

02

Implementation

We build and deploy the monitoring layer on your existing systems, on-premise, behind your firewall. Models are fitted to your own failure history and operating context rather than a generic fleet baseline. Existing control and maintenance workflows stay as they are.

03

Ongoing Support

A retainer relationship covering continuous monitoring, model refinement as conditions and duty cycles change, and reporting into your planning cycle. Your reliability and maintenance engineers hold the decisions; we keep the layer accurate and accountable.

03 / Operating Constraints

Built for sites where the cloud is not an option

Data sovereignty

Operational data stays on site and under your control. Nothing leaves the perimeter.

No cloud dependency

The layer runs on-premise and continues working through link outages.

No new sensors

We work with the tags your historian already records.

No control-system change

Read-only integration. Existing SCADA and planning workflows are untouched.

04 / Services

Three tiers. Every engagement scoped to the site.

Starting prices indicate entry points — not fixed quotes. Scope follows fleet size, data maturity, and the asset classes you prioritise.

01

Diagnostic Audit

from R100,000

Typically 2–4 weeks

A data and equipment readiness assessment against your existing SCADA, historian, and fleet. We map tag coverage, data quality, failure history availability, and which asset classes can support prediction without new sensors. You receive a clear deliverable report: what is ready, what is not, and a grounded estimate of value if you proceed to implementation.

Deliverable · Written readiness report + expected-value position by asset class

02

Implementation Sprint

from R150,000

Typically 6–12 weeks

Build and deploy the predictive monitoring system on your infrastructure — behind the firewall, on systems you already operate. Models are fitted to your operating context and failure history. Control systems and maintenance planning workflows stay intact; we add the analytical layer that reads the data continuously and surfaces developing failures before shutdown.

Deliverable · On-premise predictive monitoring layer on client infrastructure

03

Monthly Retainer

from R100,000 / month

Ongoing · monthly cycle

Ongoing monitoring support, model refinement as duty cycles and conditions change, and structured reporting for reliability and maintenance leadership. Your engineers keep decision authority; we keep the prediction layer accurate, accountable, and aligned to how the site actually plans work.

Deliverable · Monitoring, model updates, and reporting into your planning cycle

Larger fleets · multi-site

For larger fleets or multi-site operations, engagements are custom-scoped based on fleet size and data maturity — contact us for a tailored quote.

Request a tailored scope
05 / About

Built for one industry, one region, one problem.

TradeCore is a solo-founder consultancy applying structured technical depth in mining operations, industrial data systems, and applied AI — exclusively for Sub-Saharan mining.

TradeCore Solutions is led by a single founder based in Durban, South Africa. The practice combines deep, structured work across mining operations, industrial data systems (OT / SCADA / OPC-UA), and applied AI with direct relationship-building among mining technical leadership — chief engineers, reliability managers, and plant leadership who own downtime cost.

The company is deliberately narrow. TradeCore does not sell general-purpose AI workshops, chatbots, or cross-industry analytics packages. The mandate is predictive maintenance on existing plant data infrastructure for mid-cap mining operations in Sub-Saharan Africa. That constraint is intentional: in this industry, shallow breadth loses to domain depth when a crusher or haul fleet is offline and the historian already holds the signal.

Engagements stay hands-on. The founder remains the technical and commercial point of contact from diagnostic audit through deployment and retainer support — no account layers between the site and the person building the models.

Operating posture

  • Solo-founder · direct engagement
  • Base: Durban, South Africa
  • Theatre: Sub-Saharan mining only
  • Stack: OT / SCADA / historian + on-premise AI
Why we're different

Mining technical teams usually weigh two defaults before a specialist like TradeCore enters the conversation. Both have structural limits for a mid-cap, mixed-fleet operation.

Alternative A

Large global enterprise AI / analytics vendors

Enterprise platforms are built for multi-year programmes, heavy professional services, and global account structures. For a mid-cap site, that often means long procurement cycles, high minimum commercial commitment, and deployment timelines that outrun the maintenance season you are trying to protect. TradeCore scopes smaller, deploys on your existing infrastructure, and keeps the commercial surface proportional to a single operation — not a corporate transformation programme.

Alternative B

Doing nothing / OEM tools alone

Equipment OEM condition tools are useful inside a single brand envelope. They rarely give a unified view across a mixed fleet — different OEMs, different data paths, different alert logic. Relying on them alone leaves cross-fleet patterns and plant-level risk unjoined. TradeCore reads the historian and SCADA layer you already run, so prediction is not locked to one supplier's portal.

The third path is a narrow specialist who already speaks OT, maintenance planning, and failure economics — and who will not dilute that focus into a generalist AI catalogue.
06 / Insights

Notes for mining engineering leadership.

Short pieces on downtime, data readiness, deployment architecture, and maintenance economics. Full articles will be published here; titles and summaries only for now.

Reliability
Forthcoming

Reducing haul truck downtime without new sensors

How mid-cap open-pit fleets can use existing vibration, load, and run-hour streams to catch developing failures before a truck leaves the circuit.

Data readiness
Forthcoming

Data readiness for AI in mining: what chief engineers should check first

A practical checklist for tag coverage, historian quality, and failure-history gaps before any predictive model is worth commissioning.

Architecture
Forthcoming

On-premise vs cloud AI for industrial data

Why OT networks, data sovereignty, and latency constraints push predictive maintenance onto the plant side of the firewall for many Sub-Saharan sites.

Cost
Forthcoming

The economics of predictive vs reactive maintenance

Framing unplanned downtime, early component change-out, and maintenance OpEx share so technical leadership can defend investment in prediction.

07 / Contact

Request a Diagnostic Audit conversation.

Describe the operation and the equipment classes that drive downtime cost. We reply with next steps or a clear decline if the fit is wrong.

Direct

Reach the founder

Primary purpose of this form: open a Diagnostic Audit discussion. Implementation and retainer conversations follow only after readiness is established.

Enquiries are handled by engineers, not a sales desk.

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