Your machines warn you.Before they break down.72 hours ahead.
MaintainIQ continuously analyzes the vibrations, temperature and electrical current of your equipment. The AI predicts failures before they cost you and schedules maintenance automatically.
- before failure
- 72h
- maintenance cost
- −40%
- Q1 ROI
- 3x
- sensor install
- 2h
Orders of magnitude the installation aims for; they are measured on your premises during the audit, on your volumes.
Fully automated, nothing to manage.
Multi-sensor analysis
Vibrations, temperature, electrical current and ultrasound analyzed continuously. 0.001g precision. Drift detection before the critical threshold.
Failure prediction
The AI computes a health score per machine and estimates the failure window within plus or minus 6 hours. Alerts auto-escalated.
Auto work orders
The moment an anomaly is detected, MaintainIQ generates a work order, checks parts stock and pings the available technician. Zero friction.
Live in days, not months.
- 01
Install the IoT sensors
Mount the sensors on your machines in 2 hours. Guided install, no electronics expertise required. Immediate onboarding.
- 02
AI analysis and scoring
MaintainIQ analyzes the signals in real time, detects anomalies and computes a 0–100 health score per asset.
- 03
Alert and work order
72 hours before the estimated failure, your team receives an alert with the diagnosis, recommended part and technician to contact.
The pipeline behind this offer.
Kafka
Flink
PostgreSQL
PyTorch
n8n
Slack
SMSWe plug the agent into the tools you already use.
Python
ScikitLearn
Apachekafka
Pagerduty
Groq
Nothing to learn: we configure and operate these connections for you.
An unplanned breakdown is a full day of revenue gone.
Tuesday 6:12 AM. Compressor A-12 stalls, a hiss of air, then silence. Line 3 stops. The operators look at each other. The shift manager calls the industrial director, who was just replying to a top customer about a delivery delay. The part isn't in stock, the OEM technician isn't free for 36 hours, and the contractual penalty has just kicked in. What nobody will say out loud in the steering committee: that machine had been grinding for three weeks. Everyone heard it. Nobody knew when to intervene.
McKinsey has shown that reactive maintenance costs 30 to 50% more than predictive maintenance over an industrial asset's lifetime, and each hour of unplanned downtime costs between $50,000 and $250,000 in heavy industry. The global predictive maintenance market, valued at $8.6B in 2024 by MarketsandMarkets, will hit $47B by 2030, a 28% CAGR. Your German competitors already crossed that line.
Wikolabs installs AI systems built on open-source models, on your premises, paid once. No black box: your data, your prompts and your history stay in your infrastructure. We quote at a fixed price or on time and materials, never per ticket or per token, and we say before you sign what works and what does not yet.
Concretely: 2 hours to install the sensors on your critical machines, 24 hours to calibrate the model on your vibratory signature. From day 3, every asset carries a 0–100 health score, and 72 hours before an estimated failure, your maintenance lead receives an alert with diagnosis + recommended part + available technician. Documented result on our clients: −40% on maintenance costs, 3x ROI in the first quarter.
Anticipate failures 7–30 days ahead and eliminate unplanned downtime
An unplanned failure on a production line costs an average of €260,000 per hour in production losses, emergency repairs and client penalties. Scheduled preventive maintenance wastes resources on equipment that doesn't need it. AI predictive maintenance continuously analyzes vibration, temperature and electrical consumption signatures of your equipment to detect failure precursors before they occur.
Corrective maintenance costs 3–5× more than preventive maintenance. Preventive maintenance replaces still-healthy parts. Without prediction, unplanned downtime disrupts production, client deadlines and team safety. And technicians spend time on repetitive manual inspections.
Vibration, temperature and current sensors are connected to critical equipment. Data is continuously collected via the IoT pipeline and analyzed by ML models (isolation forest, LSTM, XGBoost) trained to recognize precursor signatures of failures specific to your machines. An alert is generated 7–30 days before the predicted failure, with the probable defect type and recommended urgency.
How we deploy
- 01Critical equipment identification
Criticality analysis of each piece of equipment (production impact, replacement cost, historical MTBF). Sensor deployment prioritization.
- 02Sensor & IoT pipeline deployment
Vibration, thermal and electrical sensor installation. Real-time IoT pipeline connection via MQTT or proprietary protocol.
- 03Predictive model training
Using historical failure data to train anomaly detection and RUL (Remaining Useful Life) prediction models.
- 04Alerts & CMMS integration
Predictive alert generation with defect type, urgency and recommended intervention. Integration with your CMMS (SAP PM, Maximo, Fiix).
Concrete benefits
Models detect precursor signatures well before failure. Your maintenance team plans ahead.
With early alerts, emergency shutdowns become planned stops. Production is no longer disrupted.
Less replacement of healthy parts, fewer costly emergencies, optimized spare parts inventory.
Frequently asked questions
Which equipment types benefit from predictive maintenance?
How much historical failure data is needed?
Does the solution integrate with our CMMS?
Is prediction accuracy guaranteed?
Your machines monitored starting today
Sensor install in 2 hours. First health scores within 24 hours. 14-day free trial.