Professional services for real industrial problems.
We work with asset-intensive organisations on production, quality and operational challenges – using better data, analytics and pragmatic delivery. Many engagements are accelerated by ARDI, but we always start with your problem and your existing systems.
Assessment & opportunity
Identify bottlenecks in production, quality and maintenance. Assess data sources, analytics readiness and architecture, and define practical, prioritised use cases.
Proof of Value
Rapid, focused use-case delivery on customer-specific data – evidence-backed recommendations and a low-risk path to demonstrate value before broader commitment.
Industrial analytics & AI
Predictive and diagnostic analytics, ML for quality and maintenance, process optimisation, decision support and context-driven analysis across systems.
From discovery to validated outcome, in four stages.
A short, focused engagement is the lowest-risk way to prove value before broader investment. We follow the same four stages every time – so you know what’s coming, what’s expected of you, and what you’ll have at the end.
Understand the problem
Stakeholder interviews and a review of your current systems, data and processes to identify high-value opportunities. Output: agreed problem statement, success criteria and target metrics.
Define the engagement
Select focus area and data sources, define timeline, roles and deliverables, and finalise commercial terms. Output: a signed PoV agreement with clearly defined scope, use case and success metrics.
Demonstrate measurable value
Configure the solution against your data, execute the agreed workflows, dashboards or analytics, with regular check-ins to review progress. Output: a working model, dashboard or system aligned to the agreed criteria.
Validate and recommend next steps
Present findings to business and technical stakeholders and discuss additional value opportunities. Output: customer-validated success and a recommendation for production rollout or expanded engagement.
Finding hidden causes in interconnected systems.
A manufacturer had a recurring, unexplained loss of temperature control across their gas-fired ovens – no obvious trigger and no warning. Optrix used ARDI’s Timeline addon to scan for statistically significant events around each loss-of-control. The common thread sat on the other side of the facility: a cleaning tank purge was leaving its burner valve fully open onto an empty tank, briefly starving the rest of the line of gas. There were no gas pressure sensors and only one site-wide flowmeter, so the cause was invisible on conventional trends.
hidden lead time between purge and loss of control
investigated by an analyst who had never been on site
resolved without hardware change; control quality and gas efficiency both improved
The engagement followed the four stages above – Discovery through to Review – with the root-cause hypothesis validated against historical events before any process change was made.
Different industries, same engagement model.
Each engagement starts with a problem statement and ends with validated outcomes. Across steelmaking, renewable energy, mining and manufacturing, the same four-stage process has applied – adapted to whatever the customer’s data, equipment and operational priorities required.
Coke oven battery management · Steelmaking
50 coke ovens with data dispersed across a historian, an Oracle database and Excel files at varied recording intervals. Optrix synchronised around 3,600 values across mismatched time cycles into a 3D visualisation – surfacing hidden setpoint inconsistencies, push-torque variations and data issues that existing SCADA and reports were missing.
Field access at hydro power plants · Renewable energy
Field personnel were losing time on trips back to the control room or waiting on operators for HMIs, drawings and work instructions. ARDI’s digital twin plus AR on existing mobile devices put real-time data and documentation in the field – no new hardware, faster decisions, and remote out-of-hours support for on-site staff.
Maintenance compliance · Manufacturing
Routine maintenance was thorough but hard to verify, and harder to prove for regulators. ARDI’s Checklist and Checkmate addons turned routine checks into guided, web-based checksheets with live equipment data – auditable records of who did the check, when, and what the equipment was actually doing at the time.
Remote longwall operations · Underground coal mining
A longwall mine face stretching hundreds of metres underground, with information scattered across multiple systems. ARDI’s 3D visualisation consolidated roof support heights, pressures, angles and positions into a single live view – supporting remote operations, surfacing faulty sensors, and enabling incident replay for compliance reporting.
Virtual sensor for in-tank temperature · Process manufacturing
A heating element inside a sealed liquid container relied on a failure-prone internal probe. An ARDI-trained LSTM model estimates the internal temperature from external sensors only – typically within 2°F of the actual reading at 1200°F – with discrepancy alerts that flag when the physical probe drifts or freezes, so operators can keep control and schedule a replacement.
Outcomes, not just technology delivery.
Most asset-intensive organisations don’t need more technology first. They need clarity, prioritisation and a practical path to measurable outcomes – delivered with minimal disruption and a faster time to value.
Asset-intensive experience
Deep experience in mining, manufacturing, energy and process industries – not generalist IT.
Industrial data fluency
Strong understanding of industrial data, control systems and operations – bridging engineering, operations and data teams.
ARDI or your existing stack
We start with the problem. ARDI scales the answer when it’s the right fit; otherwise we deliver inside your existing environment.
Start with a simple conversation.
We usually begin with a short discussion to understand your environment, your priorities and where better data or analytics could make the biggest impact – and what a focused PoV could prove.