Predictive Maintenance
AI flags breakdowns before they happen.
30 minutes. You leave with a written plan, whether or not you hire us.
The Problem
+70% — higher recall cost without it.
higher recall cost without it
+70%
4 hrs — response window before win rate drops.
response window before win rate drops
4 hrs
8% — of stock lost to waste.
of stock lost to waste
8%
higher recall cost without it
+70%
Included
Sensor & Equipment 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.
Tech Stack
Python
Node.js
GraphQLVs. Alternatives
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 RecommendedPurpose-built dashboards and models connected directly to your real data. | |
|---|---|---|---|
| Built for your operation | |||
| Support after go-live | |||
| Fixed, predictable timeline | |||
| No lock-in — you keep ownership |
Process
01
Weeks 1–2
02
Weeks 3–8
03
Weeks 9–12
04
Weeks 13–14
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.
Next step
We will show you the first fix and what it costs. No pitch.
30 minutes. You leave with a written plan, whether or not you hire us.
We listen
You tell us where time or money leaks. 30 minutes, your words.
We map it
We show which fix comes first and what it should cost.
You get a plan
A written plan in 48 hours. Keep it, even if you never hire us.
