Real-time Quotes/Pricing Management for Driverama Germany GmbH

A comprehensive AI driven solution for automated determination of purchase prices, including real-time monitoring of vehicle prices offered on European markets; creation of a comprehensive tool for setting a pricing strategy parameters; a robust statistical model and API to set the optimal price range for each vehicle visitor enters in a quote web form.

Client introduction

Driverama, an inventive online used-car dealership, focuses on users’ hassle free and haggle-free experience. Since their creation in late 2020, they have also opened several physical branches in Germany, formed a strategic partnership with German auto-workshop Stop + Go and became the most trusted used-car dealer on Trustpilot, averaging 4.9 stars as of 2022.

Ondřej Kuchta

“Our strategy is to provide both sellers and buyers with the best possible customer experience. Our goal was to be able to immediately provide the client with the best possible quote but still allow the company to generate sufficient profit. Determining a fair price for a buyout within the pan-European market based on a simple web form required a robust data analytics solution in the background. I am glad that there is a company like Resulmatic on the market that really understands our business and can build an end-to-end solution from data acquisition, data analysis to integration into our internal and customer applications.“

Ondřej Kuchta | Chief Technology Officer

Business Case

The client's business model is built on making the used car sales process faster and cheaper, a great customer experience right from the buying process. Offering a fair price for the car on offer to the prospective buyer immediately after the enquiry is made, and at the scale of the pan-European market, required a robust data and analytics solution. Buying a car from different countries carries different costs, different tax models are applied, and the accuracy of the parameters entered about the car is not always ideal.


Robust crawling scripts have been built to retrieve information about the prices of cars offered within selected countries as well as the prices of cars sold and store it in a cloud-based data lake. This data feeds into a comprehensive set of statistical models that, based on the specified business parameters and conditions, determine the price range of the car being bought for each local market and potential export country. A special web application has been created to manage the company's pricing policy on a regular daily basis.

Technical implementation

The entire data layer for uploading and storing market data is built on the robust Microsoft Azure Big Data cloud architecture. Machine learning models were created in Python.

The interface between data, models and web forms is provided by the rest APIs.

Captured benefits

The solution has set a reliable process of pricing the purchased vehicles in accordance with the business strategy and has become a key factor in the very positive feedback from the client's customers. The automated, self-learning algorithms consider dynamically changing conditions in individual markets, enabling the client to identify valuable opportunities with expected profitability already at the stage of buying vehicles at the optimal price for both parties.