Challenges
The biggest sources of loss in industrial maintenance.
Traditional maintenance strategies – whether reactive or rigid time-based approaches – cannot keep pace with modern industrial requirements.
Unexpected equipment failures
Unannounced breakdowns of industrial equipment cause immediate production losses. A critical machine failure can paralyze the entire production chain, while emergency repairs cost up to five times more than planned interventions.
Premature or delayed intervention
Time-based preventive maintenance ignores the machine's actual condition. This leads to unnecessary part replacements while real problems are not always identified in time – and breakdowns still occur.
Opaque equipment fleet status
Maintenance logs, Excel spreadsheets, and knowledge kept in people's heads – scattered data makes informed decision-making impossible. The maintenance manager cannot see the real condition and risks of the entire equipment fleet.
Proprietary AI Model
Our predictive model learns how your machines behave.
Safetypro is not a generic maintenance software. Our proprietary predictive model was built specifically for industrial equipment maintenance. The system builds a unique profile for each machine: it learns operational patterns, recognizes degradation signals, and predicts failures – before they happen.
Purpose-built engine
Not a generic AI solution – Safetypro's predictive model was specifically developed and optimized for industrial equipment maintenance.
Pattern recognition
The algorithm learns from the equipment's complete life history: maintenance records, fault tickets, sensor data, and operating conditions to identify failure patterns.
Early warning
The model alerts weeks before actual failure, specifying the need for intervention, its urgency, and the recommended action.
Self-improving accuracy
Every completed work order serves as feedback. The model reaches optimal accuracy within 3–6 months and continuously improves over time.
Features
Everything you need to digitize industrial maintenance.
Safetypro provides a comprehensive toolkit for predictive maintenance of industrial equipment: from condition monitoring through failure prediction to performance analytics.
Proprietary predictive model
Safetypro's in-house AI model analyzes equipment operational data and maintenance history. It recognizes failure patterns and predicts weeks ahead which machine needs attention – before the failure occurs.
Machine condition monitoring
Real-time monitoring of critical equipment parameters: temperature, vibration, pressure, energy consumption. The system instantly flags abnormal values and anomalies, enabling early intervention.
Condition-based maintenance
Schedules interventions based on actual machine condition, not rigid time intervals. The predictive model calculates the optimal maintenance timing, minimizing both downtime and unnecessary interventions.
Complete fleet management
Register and track all industrial equipment on a single platform: CNC machines, press machines, compressors, pumps, conveyors, and custom production lines. Technical data, documents, and maintenance protocols in one place.
Failure prediction
The system calculates failure probability for each piece of equipment based on historical data and real-time parameters. It creates a priority list so the maintenance team always addresses the most critical machine first.
Industrial KPI reports
MTBF, MTTR, OEE, availability, and maintenance costs by equipment group. Detailed reports prove the ROI of the predictive strategy and support investment decisions.
How it works
Predictive maintenance in four steps.
Safetypro builds a closed feedback loop: all data and experience feeds back into the predictive model, which becomes increasingly accurate.
Data collection
The system continuously collects machine operational data: sensor values, maintenance history, fault tickets, and operating parameters. Both IoT sensors and manual entry are supported.
Predictive analysis
Safetypro's proprietary AI model processes the data, identifies degradation trends, and calculates failure probability. The model continuously learns, providing increasingly accurate predictions.
Planned intervention
The system automatically generates work orders, assigns technicians, and fits maintenance into the production schedule. Technicians receive instructions, parts lists, and documentation on mobile.
Continuous learning
Every completed intervention feeds back into the predictive model. The AI learns from experience: it predicts failures more accurately and optimizes the maintenance schedule more effectively over time.
Results
Industrial results that speak for themselves.
Based on the experience of our industrial clients who switched to predictive maintenance, implementing Safetypro delivers immediate and measurable improvements in equipment reliability and operating costs.
60%
Less unplanned downtime
40%
Lower maintenance costs
3-5x
Faster fault detection
FAQ
Frequently asked questions.
Traditional maintenance software uses time-based scheduling: it generates tasks at fixed intervals (e.g., monthly, quarterly) regardless of the machine's actual condition. Safetypro's predictive model, on the other hand, learns from the equipment's actual operational data – maintenance history, sensor values, failure patterns – and predicts the need for intervention based on the machine's real condition. This eliminates both unnecessary and belated maintenance.
The model learns from equipment maintenance history and operational data. At a basic level, the history of fault tickets and completed maintenance is sufficient – this can be achieved with manual data entry. Integrating IoT sensors (temperature, vibration, pressure, energy consumption) significantly increases accuracy. The system produces reliable predictions with as little as 3–6 months of historical data.
Safetypro's predictive model predicts failures with an average accuracy of 85–92%, depending on equipment type and available data volume. The model continuously learns from new data, so accuracy improves over time. The false alarm rate typically stays below 5%, which is considered an outstanding result in the industry.
Not necessarily. The predictive model can learn from manually recorded maintenance data – fault tickets, completed work, replaced parts. Adding IoT sensors naturally increases prediction accuracy, but Safetypro can be introduced step by step: start with manual data entry, then gradually add sensors to critical equipment.
Virtually any industrial equipment: CNC machines, press machines, compressors, pumps, generators, cooling systems, conveyors, packaging machines, welding robots, and custom production lines. Thanks to flexible configuration, the system can handle any maintenance protocol and technical parameter.
Safetypro's predictive model doesn't just indicate when intervention is needed – it provides a time window: for example, “needed within the next 2–4 weeks.” This allows the maintenance manager to align the intervention with the production schedule – during shift changes, weekends, or planned shutdowns. The system automatically generates the work order for the chosen time.
The basic configuration (registering equipment, setting up workflows) takes 1–2 weeks. The predictive model immediately begins collecting data and learning. The first usable predictions appear within 4–8 weeks, and optimal model accuracy is achieved after 3–6 months. Our expert team supports you throughout the implementation and fine-tuning process.