Cluxn

Predictive Maintenance

Know a machine will fail before it does.

AI flags equipment breakdowns before they happen, so you schedule maintenance instead of reacting to downtime.

Predictive MaintenanceLive
M1
M2
M3
M4

Machine 4 — service due in 6 days

Unplanned downtime−42%

The Problem

Machines break down with zero warning, and the line stops.

No Status Visibility

0%

of manufacturers run on delayed data

Machines break down with zero warning, stopping the line for hours or days.

No Status Visibility

0%

of manufacturers run on delayed data

Maintenance happens on a fixed schedule regardless of actual equipment condition.

No Status Visibility

0%

of manufacturers run on delayed data

Nobody knows which machine is the next one likely to fail.

What's included

What's included.

Sensor & IoT Setup

Live data from the equipment that matters most.

Failure Prediction Models

Trained on your equipment's actual behaviour.

Maintenance Alerts

Warnings before failure, not after.

Downtime Dashboard

Risk and history visible on one screen.

Vs. Alternatives

Why not just use what everyone else uses?

Manual Spreadsheets

Reports compiled by hand, always at least a day behind reality.

Enterprise BI (Tableau, SAP Analytics)

Priced and built for large enterprise data teams, not a single plant.

Cluxn

Purpose-built dashboards and models connected directly to your real data.

Process

The timeline.

Weeks 1–2

Equipment Audit

The machines whose failure costs you the most

Weeks 3–8

Sensor Deployment

Monitoring installed on priority equipment first

Weeks 9–12

Model Training

Train failure-prediction models on your equipment's actual behaviour and history

Weeks 13–14

Alerts & Rollout

Alerts rolled out
Refined against real outcomes

See It In Context

See Predictive Maintenance in context.

See Predictive Maintenance in context.Live
M1
M2
M3
M4

Machine 4 — service due in 6 days

Unplanned downtime−42%

Proof

The result in practice.

Metal Castings & Foundry

Unplanned downtime reduced by flagging failure risk days in advance

Furnace failures stopped production with zero warning, each event costing a full shift of output.

Ironclad Castings

FAQ

Common questions.

Not always — some engagements start with data you already have. Where physical monitoring is needed, sensor deployment is scoped as part of the project.

Accuracy improves as models see more of your equipment's real behaviour. Most engagements start with a baseline model and refine it over 60–90 days.

The machines whose unplanned failure costs you the most in downtime or rework — the audit identifies these first.

Every engagement is scoped individually — machine count and sensor needs both affect cost. During your free assessment, we give you an exact number within 48 hours, not a guess.

Ready to fix it?

One problem. Scoped in 4 weeks.

No pitch, no generic demo — a plan built around what's actually slowing you down.

Book a 30-Minute Call ↗

30 minutes, straight to the fix. No sales script.