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
Bainten — Workshop dashboard
Workshop dashboard
Bainten — Open order list — which stage each engine is in
Open order list — which stage each engine is in
Bainten — Quality control report
Quality control report
Bainten — Part register
Part register

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 topic

Maintenance & Asset Management

Bainten

Which engine is waiting where, which job is running late: your overhaul shop on one screen, from intake to dispatch.

Work OrderITF StepsQuality Approval
ITFStep-by-step work order
LiveWorkshop dashboard
RegisterSerial number tracking
Bainten — screenshot
Industrial Operations

Bella SCADA

Monitor and control your plant from the browser: real-time SCADA with no client software to install.

MonitoringControlAlarms
OPC UAProtocol
ModbusField connectivity
WebNo client
Bella SCADA — screenshot
Automotive Analysis

DNAEksper

Has the ECU software been tampered with? Upload the file and get a structured inspection report.

AnalysisMatchingReport
AutomaticFile analysis
SHA-256Original matching
WebhookReport delivery
DNAEksper — screenshot

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.

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