Data Science

For years, we have created models for data in order to make predictions about events in the future. Advancements in machine learning and computing capacity has enabled far more sophisticated models to be created.

The applications are endless; below are just a few examples of the possibilities.

Product Classification

A major problem for retailers and eCommerce companies is categorising new products that enter the system, particularly if they stock a large number of SKUs. Furthermore, categories that may be provided by the supplier don't match those of the retailer.

Using machine learning, we can train a model on existing data to automatically sort and categorise products into the correct categories, saving hours of manual labour and increasing consistency in categories.

User Personalisation

Predicting the customer's next move enables the ability to serve content that is relevant and effective to the user. We can deploy reinforcement learning agents to learn and understand user behaviour in real-time, and predict their next most likely action - allowing you to dynamically personalise the experience for each user.

Claims Automation

The customer experience at claims can make or break a person's relationship with their insurance company. Having their application processed as quickly as possible can mean a lot to the customer during the stressful time they'd be in. We can help you create statistical and deep learning models to analyse a claim and provide straight through claims processing where the risk of fraud is low.

How We Can Help


We've deployed models to help categorise tens of thousands of products automatically for large retailers, saving hundreds of hours of manual labour. Our experience with the state of the art approaches for modelling data and practical approach to making actionable insights ensures you have a model running in production in very little time.

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