Data & AI
AI that earns out at the site, not in the slide deck. Built on foundations the plant can trust.
For COOs, GMs of operations, heads of asset management and CIOs at operators in mining, energy, marine, nuclear and defence.
In heavy industry, AI only pays when it reaches the operation, and it only reaches the operation if the foundations can carry it. Pilots do not move the numbers; production does. We meet the business at its actual maturity, build the data foundations first when that is the truth, and put AI to work where it earns its keep: planning, maintenance, readiness, reporting.
Technology in heavy industry exists to do four things.
Same four levers as every practice: the objectives every piece of data and AI work has to answer to.
Increase value
Production, throughput, and new revenue from what you already know.
Reduce expense
Unit cost down, duplication out, spend tied to return.
Control risk
Safety, security, compliance, and programs that do not surprise the board.
Manage assets
Decades-long assets, and the systems and data that run them, kept fit for purpose.
Four stages of maturity. Every one is a legitimate entry point.
Each stage pays for the next. No leapfrogging to agents on top of data the plant does not trust. Select a stage to see the work that serves it.
Data foundations & architecture
Every AI pitch you have heard assumes data your systems do not agree on.
We bring operational and enterprise data together: plant, asset, workforce, safety and commercial, with definitions everyone signs up to, on an architecture that scales with the operation. It is the unglamorous work every AI promise depends on, and if it is where your money should go first, we will say so.
AI strategy & maturity roadmap
Half the AI on your shortlist should not be bought. The hard part is knowing which half.
An honest read of where the organisation actually sits on the maturity curve, and a sequenced roadmap of use cases that move production, cost and safety numbers. What to do first, what to defer, and what to ignore regardless of the hype.
Operational & workforce intelligence
The data exists. The answers still take a week and a spreadsheet.
Dashboards and analytics that answer real operational questions: readiness gaps, mobilisation cost, control health, production performance. ESG and regulatory reporting that assembles itself from evidence the operation already captures.
Predictive maintenance & asset intelligence
Your most expensive failures announce themselves in the data long before they happen on the plant.
Digital twins, failure prediction and AI-driven planning optimisation that lift availability, extend asset life and sharpen risk visibility. Grounded in real work management and reliability frameworks, not just models.
Applied AI: pilot to production
The pilot worked. Eighteen months later it is still a pilot.
LLMs, agents and machine learning built into the workflows where work actually happens, hardened for production: offline-capable, integrated with the systems of record, and adopted on shift. This is the bridge most AI programs never cross.
AI governance, risk & responsible AI
The first question after an AI incident is who approved it. The second is on what evidence.
Data governance, model risk and the assurance that makes AI defensible to the board, the insurer and the regulator. Designed in from the start, not retrofitted after an incident.
We build AI-era products, not just decks about them.
- VerixIQ
Intelligent asset lifecycle management: from reactive backlog to risk-based, performance-driven maintenance.
- MyOPS
A single source of truth for workers, suppliers and assets, from onboarding to demobilisation.