made. Asset
A sovereign predictive maintenance engine made for asset-heavy operations — mining, infrastructure, logistics, utilities. Turn industrial IoT telemetry into forecasted equipment failure, scheduled maintenance windows, and protected margins — every prediction anchored to the underlying telemetry record and the engineering rule that triggered it.
Any operation where physical asset failure cascades into commercial loss — mining, infrastructure, logistics and warehousing, utilities, energy transmission, large-scale manufacturing, fleet operations, and port and rail. The reliability engine is asset-class agnostic; the models are tuned per failure mode (drivetrain, hydraulic, electrical, thermal, structural).
made. Asset ships with pre-trained models per failure mode and asset class — useful from day one against the common patterns. Tuning to your specific fleet typically takes 60–90 days of operational data, after which the forecasts sharpen substantially. Each confirmed failure and each avoided failure continues to harden the models from there.
made. Asset is built for edge-tolerant operation. Telemetry is buffered locally and synchronised when the link returns. Critical inference can run on edge appliances at the site, so the failure signal lands with the engineer regardless of satellite link quality. Most remote mining and rail operations are exactly this profile.
made. Asset writes work orders into Maximo, SAP PM, Pronto, Hexagon, Infor, and other major CMMS via API. Parts requisitions are handed off to made. Stack for vendor reconciliation, or to your ERP procurement module directly. Engineers keep working in the systems they already use; made. Asset adds the foresight layer underneath.
No. made. Asset is an advisory and orchestration layer — it forecasts, schedules, and drafts work orders. It does not, and will not, directly control physical equipment. Every actuation decision stays with a qualified engineer and the existing control system. The boundary is deliberate: predictive intelligence on one side, certified control systems on the other.
Every failure forecast, maintenance window, work order, and parts requisition that made. Asset produces is anchored on the made. Ledger to the underlying telemetry record and the engineering rule that triggered it — as a cryptographic, immutable audit trail. Operations engineers can trust the prediction because they can inspect the leading indicators behind it; procurement can defend the parts order because the forecast and its evidence travel together.
made. Asset runs on the same sovereign, ring-fenced architecture as every made. Platform. Operational telemetry and stored data are Australian-hosted, tenant-isolated, and encrypted at rest. Where inference runs off-shore, it runs only on Zero Data Retention providers, and your data is never used to train external public models. Edge inference is available for sites that require it. See the shared architecture.
Unscheduled equipment failure on a remote mine, a fulfilment centre conveyor, a port crane, or a transmission asset — the breakdown itself is small. The cascade is enormous: missed shifts, unfilled trucks, blown SLAs, expediting fees, and crews idle while parts are flown in.
Maintenance runs on calendar-based schedules — over-servicing healthy units and under-servicing the ones that are quietly failing. Parts inventory swells to hedge against unknown failures. The CFO wants the cost down; the GM wants the uptime up. Calendar maintenance can't do both.
made. Asset connects directly to industrial IoT telemetry, fleet management systems, and operational logs — running continuous predictive models that identify microscopic deviations weeks before they become failures.
Every sensor, all the time
Vibration, temperature, pressure, current draw, fuel consumption, load, hours, oil chemistry — made. Asset ingests the telemetry your equipment is already producing and turns it into a coherent operational signal. The data your fleet already generates becomes the data you can actually act on.
See the failure weeks before it lands
Continuous ML models trained on your fleet's behaviour identify microscopic deviations — a vibration signature drifting toward bearing wear, a thermal pattern indicating insulation breakdown — and forecast the failure window. Days or weeks of warning, not minutes.
Downtime, planned at the right moment
made. Asset orchestrates the maintenance window against operational demand, available crews, and equipment criticality. The wrench-time happens when production can absorb it — not when the failure forces it. Calendar maintenance becomes condition-based maintenance, with planning the team can defend.
The right part, ready when the job is
When the failure forecast lands, made. Asset drafts the parts requisition automatically — handed off to made. Stack for vendor reconciliation and approval. The part arrives ahead of the maintenance window. Inventory buffer shrinks; the panic-expedite call disappears.
OEM feeds, fleet management systems, SCADA, historians, CMMS, fuel and operational logs — made. Asset reads from wherever the data already lives.
Failure-mode specific models train against your historical operational data — recognising the leading indicators your engineers already half-knew.
Failure windows surface days or weeks ahead. Maintenance is scheduled into the optimal production gap. Parts are requisitioned ready for the wrench time.
Every confirmed failure and every avoided one feeds the model. The forecast horizon lengthens. The fleet's reliability compounds.
made. Asset doesn't replace your maintenance engineers. It gives them the foresight calendar maintenance never could.
Unscheduled failures collapse. The headline incidents that cascaded into millions of dollars of lost production get caught weeks ahead.
Stop over-servicing healthy assets. Stop under-servicing the failing ones. Inventory buffer shrinks. Expedite freight becomes rare.
Catastrophic in-service failure is the leading source of safety incidents on heavy assets. Forecasted failure means controlled remediation.
Runs on the made. Ledger
Every predicted failure and maintenance window is anchored to the underlying telemetry record and the engineering rule that triggered it — so operations can trust the call and procurement can defend the parts order.