Reacting to churn, demand spikes, and supply issues after they've already cost you?
Data sitting in your systems that nobody has turned into forward-looking signals?
Predictive Analytics Services
Most business decisions are made on data that's already out of date. The report shows what happened last month. The dashboard shows what's happening now. Neither tells you what's about to happen.
We build predictive analytics systems that run on your operational data -- forecasting demand, predicting churn, flagging at-risk accounts, and surfacing the signals your team needs before problems become expensive. Not dashboards that describe the past. Models that inform decisions about the future.
Demand forecasting, churn prediction, and anomaly detection on your actual operational data
Models trained on your historical data -- not generic benchmarks
Structured predictions delivered to your BI tools, CRM, or operational systems
100+ products shipped including data pipeline and AI systems
RaftLabs builds predictive analytics systems that run on your operational data to forecast demand, predict customer churn, detect anomalies, and surface risk signals before they become expensive problems. Models are trained on your historical data and integrated into your existing BI tools, CRM, or operational dashboards -- so predictions reach the people who act on them. Fixed cost, full source code ownership.
Reactive is expensive. Predictive is a system.
The direct cost of reacting to churn is the revenue you lose. The indirect cost is the marketing spend trying to replace it. The cost of reacting to a demand spike is the stockout and the expedited shipping. The cost of missing a fraud pattern is the dispute rate.
Predictive analytics doesn't eliminate uncertainty. It shifts the balance -- from finding out after the fact to having enough signal to act before the cost lands.
What we build
Demand forecasting
Forecasting models for inventory, staffing, and capacity planning. Trained on your historical order data, seasonality patterns, and relevant external signals. Weekly, daily, or hourly forecast granularity depending on operational need. Output delivered to your inventory system, planning tool, or operations dashboard. Confidence intervals so your team knows when to trust the forecast and when to apply judgment.
Customer churn prediction
Churn risk scoring for each customer or account, updated on a defined cadence. Models trained on your historical churn data -- activity signals, engagement drop-off, support patterns, and contract indicators. Risk scores delivered to your CRM so your retention team focuses on accounts that are actually at risk. Segment-level churn analysis to identify which customer profiles churn earliest and why.
Sales and revenue forecasting
Pipeline-to-close probability models for sales teams. Lead scoring that separates high-intent prospects from noise. Revenue forecasting at account, region, or product level. Trained on your CRM data and historical win/loss outcomes. Integrated into your CRM or sales dashboard so reps see a probability score on every deal without leaving their workflow.
Anomaly detection
Detection of unusual patterns in operational data -- transaction anomalies, sensor readings outside expected ranges, user behaviour that deviates from baseline, and process metrics that signal impending failure. Real-time or batch anomaly scoring depending on response time requirements. Alerting to operations or fraud teams with enough context to investigate without noise. The signal that catches problems before they become incidents.
Predictive maintenance
Equipment failure prediction for manufacturing, logistics, and facility operations. Models trained on sensor data, maintenance records, and failure history. Maintenance schedule recommendations that minimise downtime without over-servicing. Integration with your CMMS or ERP for automated work order creation. The shift from calendar-based to condition-based maintenance that reduces both unplanned downtime and unnecessary servicing costs.
Recommendation and personalisation models
Recommendation systems that surface relevant products, content, or actions for each user. Collaborative filtering, content-based, and hybrid approaches depending on your data volume and use case. A/B testing framework for model validation against business metrics. Integration with your e-commerce platform, CMS, or mobile app. The personalisation layer that improves conversion and engagement without manual curation.
Tell us what you want to predict.
Use case, current data sources, and the decision you want to improve. We'll design the model and give you a fixed cost.
Frequently asked questions
Predictive analytics uses historical data and statistical or machine learning models to forecast future outcomes -- demand levels, customer behaviour, equipment failure, fraud probability, or operational risk. Unlike descriptive analytics (what happened) or diagnostic analytics (why it happened), predictive analytics answers what's likely to happen next and with what confidence. A custom predictive analytics system is trained on your specific data, validated against your historical outcomes, and integrated into the systems where your team acts on the predictions.
The minimum is 12--18 months of historical data with the outcome you want to predict, plus the features that influence it. For churn prediction: customer activity, support history, contract data, and which customers churned. For demand forecasting: order history, seasonality, and relevant external signals. For fraud detection: transaction history with labelled fraud cases. We assess data quality, volume, and completeness during discovery. Most businesses have more usable data than they think -- the challenge is usually access and cleaning, not volume.
Accuracy depends on the predictability of the underlying process and the quality of available data. Customer churn models typically achieve 75--85% precision at 80%+ recall -- enough to focus retention effort meaningfully. Demand forecasting models for stable product categories reach 90--95% accuracy at weekly granularity. Fraud detection in financial services typically targets 85--95% precision to keep false positive rates manageable. We set accuracy targets during scoping, validate against held-out historical data, and give you the confusion matrix before deployment -- not just a headline number.
We deliver predictions wherever they're useful: a risk score added to each customer record in your CRM, a demand forecast pushed to your inventory system, an anomaly alert sent to your operations team, or a prediction dashboard in your existing BI tool. The model is only valuable if the output reaches the person who can act on it. We scope the delivery mechanism as part of the build -- including how often predictions are refreshed, what triggers an alert, and how model confidence is communicated to the recipient.
A focused predictive model -- one use case, one data source, model training and validation, and delivery to one target system -- typically runs $20,000--$50,000. Multi-model platforms with multiple forecasting use cases, automated retraining pipelines, and BI dashboard integration run $50,000--$120,000. Cost depends on data complexity, number of use cases, and delivery requirements. We scope every project before pricing it.