The most experienced operations in mining — mines, dealers, contractors — have built powerful predictive models for component lifecycle. PredictIQ is the platform that takes that knowledge out of spreadsheets and data scientists' laptops, and puts it to work across the entire fleet — with confidence bands, urgency ranking, and full audit traceability.
The knowledge exists. Experienced mines, dealers, and operators have built genuine predictive capability — regression models, failure curve analysis, wear rate projections. But that knowledge lives in a spreadsheet on one person's laptop. It cannot scale across the fleet, cannot be validated systematically against outcomes, and disappears when the person who built it moves on.
A model that requires a data scientist to run manually once a month is not an operational tool. It is a periodic report. Fleet-wide prediction requires automation, not manual effort.
A single predicted end-of-life date with no uncertainty range is worse than no prediction at all. It creates false precision. Operators need to know the optimistic, expected, and conservative scenarios.
Knowing that a component is approaching end of life is only half the problem. Knowing which of thirty components across eight machines needs attention first is the operational question — and most tools cannot answer it.
When predictive capability is embedded in a vendor's platform, the methodology belongs to the vendor. When the contract ends, the capability ends. The dealer's hard-won operational knowledge disappears with it.
PredictIQ is the execution layer for predictive models that already exist. It ingests operational data, runs your models automatically, surfaces results with confidence bands and fleet urgency ranking, and records every prediction run for audit and validation.
Operational data — telematics, oil analysis, maintenance records, condition readings — flows into PredictIQ through structured ingestion pipelines. Historical data is loaded as a backfill. Live data feeds automatically from connected sources.
Your predictive models run automatically against the current data state. No manual trigger required. Model runs are scheduled, executed, and logged without human intervention.
Every prediction produces a p10, p50, and p90 end-of-life estimate — the optimistic, expected, and conservative scenarios. Operators plan against ranges, not false precision.
Every component in the fleet is ranked by urgency. The hotsheet tells the maintenance planner exactly which components need attention first, so planning effort is concentrated where it matters most.
Remaining useful life prediction driven by machine learning models. Gradient boosted trees, CatBoost, and Monte Carlo simulation combine to produce reliable, data-driven end-of-life estimates.
Every prediction surfaces optimistic, expected, and conservative end-of-life dates. Operators plan against a range, not a single point estimate that implies false certainty.
Every tracked component ranked by urgency across the entire fleet. The maintenance planner sees immediately which components need action first — no manual comparison required.
PredictIQ hosts and operationalises your predictive models. The methodology, the training approach, and the model files belong to you — not to SeamIQ. Your competitive edge stays yours.
Every model run is logged with its full input data, model version, and output results. Any prediction can be replayed, inspected, and validated against actual outcomes — building a systematic accuracy record over time.
A printable one-page fleet hotsheet summarises the current urgency ranking, confidence band dates, and recommended actions for every tracked component — ready for site meetings and management reporting.
This is the defining commercial difference between PredictIQ and every other predictive maintenance platform on the market. We operationalise your models — we do not own them.
The approach your data scientists developed — the feature engineering, the model architecture, the training methodology — is yours. PredictIQ is the platform that runs it at scale, not the originator of it.
Model files are hosted on the platform for execution. They can be updated, versioned, and replaced by you at any time. When the contract ends, you take your models with you.
Predictive capability built from real operational data over years of fleet experience is a genuine competitive advantage. PredictIQ makes it operational and scalable — it does not commoditise it or claim credit for it.
The shift from a manually-run predictive model to an automated, fleet-wide prediction engine with confidence bands and urgency ranking is not an incremental improvement. It is the difference between a periodic report and an operational system that changes how maintenance decisions are made every day.
Tell us about your operation and we'll tell you if we can help.
Or reach out directly — info@seamiq.io
Structure, compliance, and learning at every stage of the rebuild process. From strip through dispatch, every action recorded.
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Learn more →Full lifecycle visibility across the entire asset base. Predictive and analytical intelligence from component level to fleet performance.
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