Client | A service using AI pricing strategies for setting optimal prices

TASK | Price Discrimination

Problem Background

Retailers and service providers spend an extensive amount of time and money determining the perfect price point for their products in order to maximise profit and reach. By harnessing the power of artificial intelligence, our code can find the optimal price point not only for individual products, but for individual customers, faster and with less risk than ever before.


Our newest service is a dynamic pricing tool, powered by state of the art reinforcement and machine learning. Our tool can predict the prices that individual users are willing to pay for identical products, as some clients may be more price sensitive than others. By using an individual pricing tool, any company can provide products with a higher margin to those individuals that are more willing to pay, while still retaining individuals that would otherwise seek cheaper services.

This system works at speed, with calculations performed in fractions of a second and prices changed as fast as the client can receive a signal from our server. This means that prices can be customized directly for a customer before they even reach the landing page. A customer would be entirely unaware that prices were individually set, as the prices do not change once the customer reaches the site. This system works out of the box, but improves further as more customers purchase your product and this information is fed back into our AI system. The tool is safe and secure as our servers are encrypted and don’t store any personal information. 

Our dynamic pricing tool works by embedding a javascript snippet into the client’s website, which quickly acquires a customer's information as they access the site. This information is then sent via API calls to our secure servers, which is then enriched and analysed to build a user profile. With our state of the art reinforcement learning methods the profile is then used to predict the ideal price margin for that customer. When customers purchase products, this information is fed back into our tool in order to further refine our margin prediction models


Systems have modified prices on over a hundred million dollars worth of product, with average increases in profit per user of 9% for our customers. Our solution is free to install and charges based on performance. It is accessible at