Tera Bullion builds predictive maintenance systems for energy operators: ML models trained on your equipment's own telemetry that forecast failures before they happen, feeding live operations dashboards across distributed sites. Deployments target up to a 40% reduction in unplanned downtime — turning maintenance from a reaction into a schedule.
Every operator knows the sequence: a compressor starts running hot on a Friday, nobody's watching that site's numbers, and by Monday it's down — production deferred, a crew dispatched on emergency rates, and a replacement part air-freighted because the planned-maintenance window that would have caught it was three weeks out.
The cost isn't the repair. It's the unplanned part: deferred production at thousands per hour, overtime dispatch, expedited parts, and the schedule chaos that ripples into every other job that crew was supposed to do. Deployments of the system on this page target up to a 40% reduction in unplanned downtime — which is another way of saying that a large share of the outages you're absorbing today are predictable with data you already collect.
Shortening the preventive-maintenance cycle trades one cost for another. Servicing healthy equipment on a tighter calendar burns crew hours and parts on assets that didn't need it — while the failure modes that don't respect calendars still get through.
More frequent site rounds put eyes on gauges, but a human reading a dial sees a moment, not a trend. The signature of a failing bearing lives in weeks of drift — visible in the historian, invisible on a walkthrough.
Alarm thresholds in SCADA fire when a limit is crossed — which is usually when the failure has already begun. Thresholds are binary and late; degradation is gradual and early. The gap between those two is exactly where the avoidable downtime lives.
All three share the same blind spot: the information that predicts the failure already exists in your telemetry history, and nothing in the standard toolkit actually learns from it.
Before: equipment fails → emergency dispatch → deferred production while parts and crew converge → post-mortem reveals the historian saw it coming for weeks.
After: the model flags drift early → maintenance schedules the intervention into a planned window → the part arrives by ground freight → production plans around a known four-hour service instead of absorbing an unknown forty-hour outage.
The number that moves: up to 40% of unplanned downtime converted into planned maintenance — the most expensive category of hours on your books traded for the cheapest.
If your downtime cost is real and your historians have depth, this is the highest-leverage ML deployment in energy — because the payoff arrives with the first major failure that becomes a planned repair instead.
Usually not to start. Most operators already generate more telemetry than they use — SCADA historians, run logs, work-order records. We build the first models on the data you already have, and only recommend added instrumentation where the model's blind spots justify it.
It learns your equipment's normal from historical telemetry — temperatures, pressures, vibration signatures, run hours — alongside your maintenance and failure history. When live readings drift toward the patterns that preceded past failures, it flags the asset with enough lead time to schedule the fix instead of absorbing the outage.
No. We integrate through historians, supported export paths, and APIs — read-only paths out of your operational environment. Your OT stays isolated; the analytics layer lives outside it. That boundary is a design rule, not a preference.
Less accurate than most operators assume. When an hour of unplanned downtime costs thousands in deferred production, catching even a fraction of failures early covers the build — our deployments target up to a 40% reduction in unplanned downtime, and the economics usually clear well before the model reaches its ceiling.
Weeks to a first working model if your historian data is accessible — the timeline driver is almost always data access, not modeling. The model then sharpens with every season of operation, because every run cycle is new training data.
It's exactly the case this is built for. Distributed sites are why failures surprise you — nobody can watch everything. The same pipeline that feeds the models feeds live multi-site dashboards, so operations sees equipment health everywhere without driving anywhere.
Scoped to your asset classes and data landscape, which is why we start with a free build plan. Tell us what fails, what it costs when it does, and what data you keep — we'll map the build and the number it has to beat.
Tell us how this works in your operation today. We'll send back a build plan — no pitch deck, no fluff, just engineering.
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