The AI decision layer for heavy trailer fleets

Know what your trailers need next.

Acciaio Lab connects trailer telemetry, component trends and maintenance history so operators can see risk earlier and act with evidence.

In pilot with a North Queensland haulage company.

Secure data layerControlled accessHuman-approved actions
Live trailer viewAL active
Technical line drawing of a three-trailer road train
TR4278
Inspect · 7 days
TR4395
Stable
TR4490
Monitor
Action neededMonitorStableExample data
Reduce downtime
Track component wear
Prioritise inspections
Protect fleet data

Start where you sit

Three teams, one dataset.

Maintenance

What do I inspect next?

Ranked inspection priorities with the trend, route and load evidence attached, so workshop time goes where the risk is.

See use cases →
Operations

Which assets stay available?

Early warning on the trailers and routes most likely to take a unit out of service before the next haul cycle.

Meet AL →
Sales & commercial

What is the return?

Downtime avoided, asset life extended and a defensible maintenance record for resale and customer reporting.

See the business case →

What the system does

Four layers, one trailer data chain.

From CAN bus capture on the trailer to a ranked maintenance action in the workshop, each layer is defined, testable and owned by the customer.

01

CAN bus sensor package

Trailer-mounted hardware captures motion, load, braking, air spring and GPS context on a generic, fleet-ready hardware layer.

02

Secure data layer

Data is validated, organised and protected before it becomes fleet intelligence. Security is a core layer, not an add-on.

03

Digital twin analytics

Working calculations convert trailer behaviour into health, load, fatigue, route severity and maintenance context.

04

AL decision layer

AL explains risk, ranks maintenance priorities and produces evidence-backed actions for fleet and workshop teams.

Hybrid dashboard

Component trends before downtime.

Fleet health, active risks and route severity sit alongside component-level trend tracking, so maintenance and operations read the same evidence.

82Fleet health
14Active risks
4.2Route severity
Component trend trackingExample data
30 day movement
Kingpin wear index+18% trend · Inspect
Suspension fatigue+12% trend · Monitor
Brake pressure variance+6% trend · Review
Axle load balance−4% trend · Stable

AL: T14 is the next inspection priority. Kingpin wear trend and high-load events are moving above normal.