Predictive Analytics & Time-Series Forecasting

Turn Your Data Into Business Decisions

We build predictive models that actually work in production—not demos that fail when real data arrives. Our focus is on practical forecasting that drives business value.

Our Predictive Analytics Services

• Demand forecasting — Inventory optimization, supply chain planning, resource allocation
• Churn prediction — Identify at-risk customers before they leave for SaaS and subscription businesses
• Anomaly detection — Fraud detection, system monitoring, quality control alerts
• Time-series forecasting — Financial planning, capacity planning, trend analysis

Our Approach to Predictive Analytics

Data Assessment

We audit your existing data, identify gaps, and build the pipelines needed to feed reliable predictions. Bad data in means bad predictions out—we fix this first.

Model Development

Feature engineering, model selection, and rigorous validation. We test on realistic scenarios, not just clean holdout sets. Our models use probabilistic approaches that quantify uncertainty—you know how confident each prediction is.

Production Deployment

MLOps, monitoring, and retraining pipelines. Your models stay accurate as data drifts over time. We don't hand off a Jupyter notebook and disappear.

Industries We Serve

• E-commerce & Retail — Demand forecasting, inventory optimization
• SaaS & Subscriptions — Churn prediction, LTV modeling
• Finance & Fintech — Risk scoring, fraud detection, market forecasting
• Logistics & Operations — Capacity planning, route optimization

FAQ

How much data do I need for predictive analytics?

It depends on the problem. For time-series forecasting, we typically want at least 2-3 years of historical data. For classification problems like churn, a few thousand examples can work. We'll assess your data in the first week.

How long until I see results?

Initial models can be validated in 4-6 weeks. Production deployment typically takes another 2-4 weeks depending on your infrastructure. You'll see preliminary insights within the first month.

What if my predictions are wrong?

All predictions are probabilistic—we quantify uncertainty so you know how much to trust each forecast. We also build monitoring to catch when model performance degrades and trigger retraining.