Predictive Analytics Development

Machine learning models and data systems for businesses across the UK and Isle of Man. Forecast outcomes, identify patterns, and make better decisions with your operational data.

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Data-Driven Decisions

We design and build predictive analytics systems for businesses across the UK and Isle of Man. Machine learning models and data systems that forecast outcomes, identify patterns in your operational data, surface risks before they materialise, and provide the quantitative basis for decisions that currently rely on experience and intuition.

Predictive analytics delivers value when it is built on good data, applied to the right problems, and integrated into the workflows where decisions are actually made. A predictive model that lives in a data scientist's notebook and requires manual operation does not change how your business operates. A predictive model embedded in your operational software - surfacing its predictions at the moment a decision needs to be made - does.

Every predictive analytics system we build is designed and delivered personally by Owen Jones, OLXR's founder and lead engineer. We treat predictive analytics as an engineering discipline first and a data science exercise second - because a model that does not work reliably in production, at scale, integrated with your operational systems, is not a solution.

Who This Is For

Businesses that want to forecast demand, revenue, or other operational metrics with more accuracy than manual estimation

Organisations that want to identify which customers are at risk of churning before they leave

Companies with fraud or anomaly detection requirements that cannot be met by simple rule-based systems

Businesses that want to optimise pricing, inventory, or resource allocation based on predicted demand

Teams that have accumulated significant operational data and want to extract systematic insight from it

Organisations that want to move from reactive to proactive operations

What We Deliver

Demand Forecasting

Models that predict future volumes, revenue, or demand based on historical patterns.

Churn Prediction

Models that identify customers at elevated risk of cancellation in time for intervention.

Anomaly Detection

Systems that identify unusual patterns in operational data.

Recommendation Systems

Models that predict what a user or customer is most likely to want next.

Risk Scoring

Models that assess the risk level of customers, transactions, or decisions.

Operational Optimisation

Models that recommend optimal pricing, scheduling, or resource allocation.

Model Monitoring

Ongoing tracking of model performance with alerting when performance degrades.

Model Experimentation

Structured experimentation with different algorithms and features to find the approach that performs best on your specific data.

Our Approach

Start with the Decision, Not the Data

The most common failure mode in predictive analytics is building a model first and then trying to find a use for its predictions. We start with the decisions your business makes - which decisions would be better made with a reliable prediction, what information is available at the time that decision needs to be made, and what the cost of a wrong prediction is relative to no prediction at all. That framing determines whether predictive analytics is the right approach and what the model needs to predict.

Build for Production

A predictive model that works on historical data in a development environment is not the same as one that works reliably in production on live data at scale. We build the data pipelines that feed the model with current data, the serving infrastructure that makes predictions available to your application in real time, and the monitoring that tracks model performance over time. The model is one component of a production system, not a standalone deliverable.

Monitor and Retrain

Predictive models degrade over time as the world changes and the patterns they were trained on become less representative of current conditions. We build monitoring into every predictive analytics system that tracks model accuracy in production and alerts when performance falls below acceptable thresholds. We also design for retraining - the process of updating the model with new data - to be a routine operational activity rather than a significant engineering project.

Why Choose OLXR

We treat predictive analytics as an engineering discipline first. A model that works in a notebook but not in production is not a solution. We build the complete system - from data pipeline to serving infrastructure to monitoring.

Senior-Led

End-to-end from data engineering to production deployment

Production-First

Models built to work in real systems, not just notebooks

Monitored

Performance tracking and alerting in production

Honest Assessment

If your data is not ready, we will tell you

A predictive model that lives in a data scientist's notebook and requires manual operation does not change how your business operates.

OJ

Owen Jones

Founder & Lead Engineer

Technologies We Use

Python

ASP.NET Core

SQL Server

PostgreSQL

AWS

Azure

REST APIs

Vector Databases

EF Core

Don't see your stack? Get in touch.

Frequently Asked Questions

The amount of data needed depends on the complexity of what is being predicted and the signal-to-noise ratio in the data. Simple forecasting models can work with a few hundred data points. Complex classification models typically need thousands. We assess your data during the discovery phase and give you an honest view of whether you have enough data for the approach you are considering, or whether data collection needs to happen before modelling is viable.

Prediction accuracy depends on how predictable the thing being predicted actually is, the quality and quantity of the data available, and the features available at prediction time. We do not promise specific accuracy levels before seeing your data - anyone who does is not being honest. We conduct a feasibility assessment during discovery and give you a realistic view of the accuracy that is achievable before committing to a build.

Predictions are delivered to your existing systems through an API that your application queries at the point a decision needs to be made. We build the serving infrastructure that makes predictions available with the latency your use case requires - from batch predictions computed overnight to real-time predictions with sub-second response times. The integration approach depends on your application architecture and the timing requirements of the decision.

Related Services

AI Solutions

Practical AI solutions for your business

Database Design

Structured data foundations for analytics

Reporting & Analytics

Dashboards and reporting systems

Ready to Build Predictive Analytics?

Tell us what decisions you want to make better. We will give you an honest view of whether predictive analytics is the right approach and what your data can realistically support.