Maintenance & Asset Management
Bainten
Maintenance management that brings equipment, maintenance plans and failure history together in one place.
Maintenance PlanWork OrderAssets
PeriodicMaintenance
MobileField entry
BarcodeAsset tracking
Machine Learning · Ankara
Building a prediction model is easy; keeping it right in the field is hard.Machine Learning (ML) Solutions
We develop models for predictive maintenance, demand forecasting and quality prediction. Putting a model into the field is only the beginning; the real issue is noticing when it degrades over time.
Predictive maintenanceDemand forecastingAnomaly detectionQuality predictionData pipelinePerformance monitoring
In machine learning projects, the success criterion is usually set wrong: accuracy on training data. The real question is whether the model performs the same in production — and who notices when it doesn't.
That's why we build its surroundings as carefully as the model: data pipeline, performance monitoring, a retraining routine and a human approval point. Without them, a model quietly becomes unreliable within a few months.
If this sounds familiar
- Maintenance follows the calendar; equipment sometimes fails before its service is due.
- Demand or consumption forecasts rely on the intuition of one experienced person.
- Quality deviations are noticed only after the product is tested.
- A model was built once, but nobody knows whether it still predicts correctly.
What it covers
Predictive maintenanceWear signals from equipment measurements; moving from calendar-based to condition-based maintenance.
Anomaly detectionCatching deviation from normal — in processes, energy consumption or transaction records.
Forecasting modelsDemand, consumption and duration forecasts. Uncertainty is reported too: a range instead of a single number.
Data pipelinePreparing the data that feeds the model in a regular, repeatable and traceable way.
Performance monitoringContinuous comparison of predictions with real outcomes in the field; degradation raises an alarm.
RetrainingMaking when and with which data the model is refreshed part of the routine.
Example scenarios
Illustrative examples; the specifics are worked out during discovery at your site.
MaintenanceEarly warning from vibration and currentThe normal behaviour of motor and pump readings is learned; as deviation grows a warning is raised and a work order is suggested to the maintenance team.
PlanningRange-based demand forecastingDemand per product and region is forecast as a range instead of a single number, so planning also sees how large the uncertainty is.
ProcessCatching abnormal consumptionEnergy or raw material use is compared with the value expected under current production conditions; unexplained deviations go up for review.
Bainten screens
Bainten page →
What we deliver
- ✓Problem definition and error-cost analysis
- ✓Data sufficiency assessment
- ✓Model result compared against a simple baseline
- ✓Model and data pipeline running in production
- ✓Performance monitoring dashboard and drift alarm
- ✓Retraining routine and documentation
How we proceed
1 · Problem definitionWhat are we predicting, which decision does it feed, and what does an error cost?
2 · Data assessmentIs there enough history, are there labels? Most projects change scope at this step.
3 · BaselineComparison with a simple rule or statistic. If the model can't beat it, there's no need for a model.
4 · Production and monitoringDeployment to the field, performance measurement and the retraining cycle.
When it's the right choice
- There is a history of what you want to predict, and the outcome can be measured afterwards.
- The value of a correct prediction covers the cost of building and maintaining the model.
- The prediction feeds a decision, and the person making that decision is involved.
When it's not the right choice
- If there aren't enough past examples (for instance, almost no failure records) — data has to be collected first.
- If a simple rule gives the same result.
- If it isn't clear whose decision the prediction will feed.
Key products for this industry
Industrial Operations
Bella SCADA
Browser-based real-time monitoring and control for industrial facilities.
MonitoringControlAlarms
OPC UAProtocol
ModbusField connectivity
WebNo client
Automotive Analysis
DNAEksper
A system that analyses ECU software files and produces a structured inspection report.
AnalysisIntegrityReport
AutomaticFile analysis
HashIntegrity
WebhookReport delivery
Frequently asked
How much historical data is needed?+
It depends on the problem. In predictive maintenance what matters isn't volume but variety: there must be enough real failure examples for the model to learn from. This is measured concretely in the data assessment step.
How do we know the model is useful?+
By comparing it with a simple baseline. If it isn't clearly better than the existing rule or expert estimate, the cost of building a model doesn't pay off — and we say so up front.
Let's talk about this.
We'll listen to your current situation and work out together where to start.