It depends on the complexity, but here are general guidelines:

For simple classification (lead scoring, basic churn): 5,000 to 10,000 records minimum, 50,000+ ideal.

For complex patterns (fraud detection, demand forecasting): 50,000 to 100,000 records minimum, 500,000+ ideal.

For deep learning: 100,000+ minimum, millions ideal.

More important than quantity is quality. 10,000 clean, relevant records beats 1 million noisy ones. During our Discovery phase, we assess your data and tell you honestly if you have enough.

What’s the difference between ML and traditional analytics?

Traditional analytics answers: What happened? Why did it happen?

Machine learning answers: What will happen? What should we do?

Traditional analytics looks backward. It tells you that sales dropped 15% last quarter and helps you understand why.

Machine learning looks forward. It tells you which customers will churn next month, which products will sell out, which transactions are fraudulent.

Both are valuable. Most companies need both. We often start with analytics (dashboards) and add ML when the foundation is solid.

Discovery phase: 1-2 weeks. You’ll know if ML is feasible for your use case.

POC with accuracy metrics: 4-6 weeks. You’ll see real numbers on your real data.

Production deployment: 10-16 weeks total from start.

ROI realization: Typically 2-6 months after deployment, depending on use case.

The POC phase is designed to prove value quickly. If it doesn’t hit accuracy targets, you don’t proceed to production.

No. We design for low-maintenance operation.

What’s automated: Model retraining on schedule, data drift monitoring, performance alerts, basic issue detection.

What you need: Someone to review alerts (can be a business user), domain experts to validate edge cases, IT support for infrastructure (or we can host it).

Optional: Our MLOps retainer  handles all ongoing maintenance if you don’t want to manage it internally. Most clients without data science teams choose this option.

ML models can degrade as patterns change. We address this with:

Monitoring: We track prediction accuracy continuously, detect data drift (when input patterns change), and alert you when performance drops below thresholds.

Retraining: Models are retrained on schedule (weekly, monthly) or triggered when drift is detected. New models are tested before replacing old ones.

Human oversight: Regular performance reviews, feedback loops from end users, and quarterly model audits.

This is all included in our MLOps retainer, or we train your team to do it.

Yes. We design for integration, not replacement.

Common integration patterns: REST API (your app calls our model), batch scoring (we score data on schedule), CRM integration (predictions in Salesforce, HubSpot), ERP integration (forecasts in SAP, Oracle), webhooks (trigger actions based on predictions).

We’ve integrated with virtually every major system. If it has an API or database access, we can connect to it.

We define ROI metrics during Discovery, before building anything.

For churn prediction: Revenue retained minus project cost. If we reduce churn from 18% to 12% on a €10M customer base, that’s €600K saved annually.

For fraud detection: Fraud prevented minus project cost plus false positive reduction value.

For demand forecasting: Inventory cost savings (reduced stockouts plus reduced overstock).

For recommendations: Incremental revenue attributed to recommendations minus project cost.

We build ROI tracking into every deployment so you can see value continuously, not just at the en

We’ve deployed ML models across:

Financial services: Fraud detection, credit scoring, churn prediction, customer segmentation, AML monitoring.

Retail and e-commerce: Demand forecasting, recommendation engines, price optimization, customer lifetime value.

Telecommunications: Churn prediction, network optimization, customer segmentation.

Manufacturing: Predictive maintenance, quality prediction, demand forecasting, supply chain optimization.

Healthcare: Patient risk stratification, readmission prediction, resource optimization.

Energy and utilities: Consumption forecasting, anomaly detection, predictive maintenance.

The algorithms are similar across industries. What changes is domain knowledge and feature engineering. We bring both.