Rules-based system that can't keep up with the complexity and variability of your real data?
ML model built by a previous team that no one is monitoring or maintaining in production?
Machine Learning Development
Custom machine learning models solve prediction, classification, anomaly detection, and recommendation problems that rules-based systems cannot -- because the patterns in your data are too complex, too variable, or too numerous to express as explicit logic.
We build ML models trained on your data for your specific problem: churn prediction, demand forecasting, fraud detection, pricing optimisation, and anomaly detection. Data audit, feature engineering, model training, evaluation, and production deployment with monitoring so you know when model performance drifts.
Custom ML models trained on your data -- not generic off-the-shelf models
Prediction, classification, anomaly detection, and recommendation systems
Production deployment with monitoring so you catch performance drift early
Data audit first -- we tell you if your data is sufficient before building
Machine learning development involves training custom models on your historical data to predict outcomes, classify inputs, detect anomalies, or generate recommendations. It is the right approach when your problem has clear input-output relationships and sufficient labelled historical data, but the patterns are too complex or numerous to express as explicit rules. Common business applications include customer churn prediction, demand forecasting, fraud detection, and dynamic pricing.
Machine learning is most valuable when your problem has a pattern in historical data that predicts something you need to know -- but that pattern is too complex, too variable, or too deeply buried in interactions between features for a human to extract as explicit rules. Churn prediction, demand forecasting, anomaly detection, and fraud scoring are canonical examples: there are signals in the data, but no simple threshold or rule captures them reliably.
The discipline in ML development is not in choosing an algorithm -- that's the last decision, not the first. It's in understanding your data well enough to know whether a model is feasible, engineering features that give the model something useful to learn from, and building the evaluation and monitoring infrastructure to know whether the model is actually working after deployment.
What we build
Classification and prediction models
Binary and multi-class classification models for problems with clear outcome categories: fraud or legitimate, churn or retained, approved or declined, high-priority or routine. Probability outputs rather than hard classifications for cases where ranking and scoring are more useful than binary decisions. Feature importance analysis and model explainability for regulated use cases or internal sign-off requirements. Trained on your labelled historical data, evaluated on held-out data, deployed with defined performance thresholds.
Demand forecasting systems
Time-series forecasting models for demand, sales volume, resource usage, and capacity planning. Handles seasonality, trend, and external factors (promotions, weather, calendar events) that simple averaging misses. Multi-horizon forecasts at product, location, or segment level. Uncertainty quantification so planners know the confidence interval, not just the point estimate. Integrated with your planning systems or ERP so forecasts drive actual decisions rather than sitting in a report.
Churn prediction models
Churn prediction models that score every customer on their likelihood of leaving before they do -- giving your retention team an actionable list ranked by risk. Feature engineering from your product usage, support history, billing, and engagement data. Threshold tuning calibrated to your intervention capacity and the cost of false positives. Regular retraining pipelines so the model adapts as product behaviour evolves.
Anomaly detection pipelines
Statistical and ML-based anomaly detection for fraud, equipment failure prediction, network security, and process quality control. Supervised anomaly detection when you have labelled examples of known anomalies; unsupervised methods when anomalies are unknown or rare. Real-time scoring for time-sensitive detection and batch scoring for periodic review queues. Tunable sensitivity thresholds to balance false positive rate against detection coverage.
Recommendation systems
Collaborative filtering, content-based, and hybrid recommendation models for product discovery, content personalisation, and cross-sell. Cold-start handling for new users and new items without interaction history. Real-time and batch recommendation generation depending on your latency and infrastructure requirements. A/B testing infrastructure to measure recommendation quality against a baseline, not just offline metrics.
MLOps and model monitoring
Production deployment infrastructure for ML models: model serving APIs, versioning, blue-green deployment for model updates, and rollback capability. Data drift monitoring and model performance monitoring with alerting. Automated retraining pipelines triggered by drift signals. Feature store integration for consistent feature computation between training and serving. The operational infrastructure that keeps ML models accurate and maintainable after the first deployment.
Pattern in your data that your rules aren't capturing?
Tell us the prediction problem, what data you have, and what decisions the model output needs to drive. We'll audit the data and give you a feasibility assessment before scoping a build.
Related AI development services
AI Development -- overview of all AI development capabilities
RAG Pipeline Development -- RAG for knowledge retrieval alongside ML systems
AI Agents -- AI agents that use ML model outputs as part of multi-step workflows
Computer Vision -- computer vision models for image and video analysis
Related services
Machine Learning Development -- extended ML development coverage and case studies
Predictive Analytics -- business-focused predictive analytics and forecasting
Frequently asked questions
A rules-based system is faster to build and easier to explain, but it fails when the patterns you need to capture are too complex, too variable, or too numerous to express as explicit if-then logic. ML makes sense when: you have a clear input-output relationship but too many interacting variables for rules to cover reliably; your rules require constant manual updating as the world changes; you need to rank or score items (leads, transactions, customers) rather than binary classify; or you've already tried rules and they're not performing well enough. The honest answer is that many problems are better served by improved rules or simple statistics -- we'll tell you that during scoping rather than recommending ML unnecessarily.
You need labelled historical data: examples of the input features alongside the outcome you're trying to predict. For classification -- churn, fraud, default -- you need enough positive examples of the event you care about (typically hundreds to thousands, not just a handful). For regression -- demand, pricing, revenue -- you need sufficient historical range across the conditions you'll encounter in production. Data quality matters more than volume: clean, consistent, representative data with accurate labels outperforms a large dataset with noise and label errors. We run a data audit before scoping the model build -- we won't recommend proceeding if the data isn't sufficient.
Models degrade because the real world changes -- customer behaviour shifts, seasonality patterns change, new product lines or markets don't match the training distribution. We deploy models with monitoring for two types of drift: data drift (the distribution of input features is changing) and performance drift (model predictions are becoming less accurate against ground truth). We set alerting thresholds and retraining triggers, and we build the retraining pipeline before deployment so when drift is detected, retraining is a defined process rather than a scramble. Production ML without monitoring is not production ML.
A single ML model -- data audit, feature engineering, training, evaluation, and production deployment with monitoring -- typically runs $25,000--$80,000. Complex ML pipelines with multiple models, real-time inference infrastructure, A/B testing, and full MLOps setup run $80,000--$200,000. Cost depends on data complexity, model type, infrastructure requirements, and monitoring depth. We scope before pricing and deliver a fixed-cost proposal after a data audit confirms the feasibility of the build.