Mining operations generate sensor, fleet and process data on every shift, and the opportunity is to turn that flow into decisions your teams can act on. Lumina connects condition monitoring, maintenance and production data into a structured reasoning layer, so your engineers see what matters with the evidence behind it.
See How It Works
Watch six specialized agents reason over one asset in plain language, with the reasoning attached, the way a mine moves from condition monitoring to prescriptive operations.
The Strategic Gap
Mining runs on rotating equipment, and the disciplines that keep it running are mature and well staffed. What has changed is the volume of information arriving ahead of each failure, because instrumentation became inexpensive faster than the hours available to interpret it. These are the six places where the return leaks away.
An ultra class haul truck costs several million dollars, and unplanned downtime on one is commonly costed at around $180,000 per hour. The interval between the first detectable symptom and the failure itself is where that cost is either avoided or paid, and avoiding well under an hour of it covers what a program like this costs.
A condition monitoring program is judged by the assets it actually reviewed, rather than by the assets it was scoped to cover. When the queue outgrows the hours, attention concentrates on familiar machines and the rest are inspected after they fail.
A site that once walked a monthly route now receives readings from hundreds of measurement points, each carrying several parameters across three axes. Trending all of it and comparing each parameter against its setpoints fills the morning before any diagnosis begins.
A finding earns nothing until someone schedules the work, orders the part and books the window. Recommendations that arrive without the evidence behind them are the ones that wait for a second opinion, and the interval closes while they wait.
Speeds, bearing details and alarm setpoints are usually entered once at commissioning. Motors get rewound and thresholds get tuned during a busy shift, and every figure derived from that record quietly inherits the difference.
The reasoning behind a good call tends to live with a few experienced engineers, and much of it leaves the site when they retire or move on. The next analyst starts the same investigation from the beginning.
What Lumina Does
Lumina does not replace your engineers or your existing systems. It adds a reasoning layer on top, turning the data you already collect into intelligence your teams can act on, verify, and trust.
Specialist agents work the way a certified analyst or a production superintendent does, in the vocabulary your teams already use, and they show the evidence behind every conclusion.
Severity zones, defect frequencies, crest factor and the days remaining before a threshold is reached are calculated by the analytical engine. The charts are built from your measurements, and the agent describes what it sees rather than drawing the picture itself.
The rules, thresholds and standards your experts spent years building live outside the model, owned by you. Change a rule and you change the behaviour, audit a rule and you audit the system, because that logic is your intellectual property.
What a senior engineer teaches the system stays with the team afterwards. Corrections to a machine record and judgments about how an asset behaves shape the next analysis, so understanding accumulates instead of resetting.
When reliability and production pull in different directions, the specialists argue it out on the record and one recommendation comes back. The veto belongs to the role that owns the risk, and it is enforced rather than negotiated.
The analysis runs on the hardware you already own, so the working dataset is processed where it sits. You choose how your data interacts with AI, and you can point Lumina at a model inside your own network.
How It Works
Most AI tools are a wrapper around a single language model. Lumina is built on DIF, so working with your data is a reasoning dialogue: a plain-language question goes in, and a traceable, computed answer comes out.
Every answer is one you can inspect, not a black box. And the memory the system builds as you work compounds over time, so each question starts from everything your team has already asked.
Use Cases by Domain
Condition Monitoring, The Machine Record, Daily Review
Rotating equipment announces a developing fault across a detectable interval, and the value of the whole program lives inside that notice period. Lumina reads the measurements you already collect, computes the engineering, and puts the evidence beside every recommendation so the call can be made and defended on the same morning.
An ISO 18436 trained analyst copilot reads spectra, trends and waveforms, computes ISO 10816 severity from the machine's own velocity readings, and separates imbalance, misalignment, looseness and bearing wear by their signatures. Charts mark harmonic orders and, where the bearing geometry is on file, its defect frequencies with a tolerance band around each.
Speeds, bearing details, alarm setpoints and lifecycle status live in a sensor register your own engineers maintain. Every change carries its author and its reason, threshold edits require one before they save, and corrections flow into the analysis that follows.
Export the telemetry your fleet management system already records and ask questions of it directly. Lumina reads the file before you ask anything, surfacing outliers, trend direction, sudden step changes and which variables move together and with what delay.
Plan Versus Actual, Shift Performance, Plant Effectiveness
Throughput is decided by many small variances that are already visible in data your site produces every shift. Lumina works over your shift reports, production logs and plant exports, computing every figure from your rows rather than summarising a sample, so a variance can be attributed rather than argued about.
Where your data holds a planned value and an achieved value side by side, Lumina detects whether one consistently runs high or low, quantifies the gap and plots it against the ideal line. Systematic bias becomes visible instead of being absorbed into the monthly average.
Where a crew, shift, pit or line column exists, Lumina finds which ones run above and below the site average on tonnes, recovery or any other measure, and by how much. A production superintendent can attribute a gap to a cause and rank crews on unplanned events.
A throughput number breaks into availability, rate and quality, and Lumina finds the operating settings that coincided with your best runs so they can be repeated. Comparisons run across shifts and campaigns on the data you already export.
Across the Enterprise
Reliability and throughput sit inside a wider operation, and the data that explains them is usually held by other functions. Lumina applies the same approach to the tables those teams already produce, so a question can be answered without waiting for a report to be built.
Track spend against plan, cost per tonne and the balance between planned and unplanned work, using the exports your finance and planning systems already produce. Variance analysis explains why a number moved rather than only reporting that it did.
A reading is only as good as the device that produced it, so Lumina scores sensor quality, watches battery and signal strength, and reports how recent each point's last capture is. A quiet sensor is reported as quiet rather than read as a healthy machine.
Load incident records, near miss reports and hazard observations and look for the patterns inside them. Correlations between operating conditions and incident history become visible to the people who can act on them.
Where roster, shift and training records exist as data, Lumina compares them against production and reliability outcomes, so decisions about crewing and scheduling can rest on the site's own history.
Deployment
Five deployment levels from public cloud to fully air-gapped. Bring your own LLM or use ours. Export agents to operate inside Claude, Copilot, or any MCP-compatible tool.
Bring one sensor file or one production export, and we will show you the analysis, the evidence behind it and the questions your team would ask next.
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