Patterns in your data you're not reading yet.
Churn prediction, demand forecasting, anomaly detection, and customer segmentation — built on your historical data, producing numbers your ops team can act on.
Classical machine learning — regression, classification, clustering, anomaly detection — remains the most commercially reliable form of AI for structured data problems. It's interpretable, auditable, fast to deploy, and cheap to run. Most businesses with 12+ months of operational data have enough signal to build models that outperform human intuition on forecasting and risk problems. We find those signals and build the production pipelines around them.
15–40%
typical improvement in forecast accuracy vs. rule-based systems
6 wks
median time from raw data to production churn model
3–7×
ROI on predictive maintenance vs. reactive maintenance
What's included
Services within Classical ML & Predictive Analytics
Each is a scoped engagement. Tell us which one fits your situation — or book a call and we'll scope it together.
Regression & Demand Forecasting
Sales forecasting, inventory demand prediction, and revenue projection models using linear regression, gradient boosting, and ensemble methods — with confidence intervals your planning team can use.
Classification Systems
Binary and multi-class classifiers for lead scoring, fraud detection, credit risk, document routing, and medical triage — with calibrated probability outputs and decision thresholds optimised for your cost function.
Clustering & Customer Segmentation
K-means, DBSCAN, and hierarchical clustering for customer cohort discovery, product affinity grouping, and geographic market segmentation — with segment profiles your marketing team can act on.
Anomaly Detection
Statistical and ML-based anomaly detectors for fraud, network intrusion, equipment failure, and financial reporting irregularities — tuned for your false-positive tolerance.
Churn Prediction
Customer churn models trained on behavioural, transactional, and engagement signals — with feature importance explanations and CRM integration to trigger retention workflows automatically.
Time Series Analysis
ARIMA, Prophet, and LSTM-based time series models for energy consumption, website traffic, financial metrics, and sensor data — with seasonal decomposition and exogenous variable support.
Causal Inference
Difference-in-differences, propensity score matching, and instrumental variable methods to answer 'did this intervention actually work?' — for marketing attribution, policy evaluation, and A/B test analysis.
Feature Engineering & Data Preparation
End-to-end feature pipelines: imputation, encoding, interaction terms, lag features, rolling aggregates — built as reusable, version-controlled transformations that work the same in training and inference.
The problem
Where classical ML projects go wrong
These aren't edge cases — they're what we hear on almost every discovery call. If any of them sound familiar, this is likely the right place to start.
Feature engineering is underestimated — the model is only as good as the variables you feed it, and this is 60% of the work
Training on imbalanced datasets (1% churn rate, 0.1% fraud rate) produces misleading accuracy metrics and useless models
Models trained on one time period fail silently when business conditions change — without monitoring, degradation goes undetected
Interpretability is neglected — a black-box model that ops teams don't trust won't get used, regardless of accuracy
Data leakage during feature construction inflates validation metrics and produces models that fail the moment they hit live data
Who it's for
This is the right fit if…
These systems work best for organisations at a specific point — where the problem is real, the data exists, and generic tools have already proved insufficient.
Retailers and e-commerce businesses with 12+ months of transaction history
Financial services firms managing credit, fraud, or portfolio risk
SaaS companies with subscription churn they want to predict before it happens
Manufacturers tracking equipment health and maintenance cycles
Marketing teams spending budget without knowing what's actually driving conversion
Common questions
What people ask before they book
Not sure where to start?
Talk it through on a free call.
We'll help you figure out which of these fits your situation — no pressure, no obligation.
Book a Free 30-Min Call