Driving Operational Excellence in the Food and Beverage Sector
Aeroblue Software specialises in enhancing business performance through services like optimised customer pricing, sales optimisation, production, and quality control. Their SaaS platform, CleverFoodie, is tailored to the food and beverage industry, focusing on peer-to-peer customer pricing and profit margin optimisation to streamline operations and reduce waste.
The Challenge
Aeroblue Software observed that the SME food sector is poorly served by technology vendors. Many technology providers lack the tools to analyse collected data effectively, leaving businesses without insights to address quality control issues, identify production bottlenecks, or uncover sales opportunities. This lack of analysis limits operational efficiency and strategic decision-making.
The Solution
- Aeroblue Software collaborated with the Hartree Centre Northern Ireland Hub to develop a machine-learning-driven sales mark-up recommender system. This system uses historical pricing, sales, and customer behaviour data to suggest competitive peer-to-peer price adjustments, enabling customers to optimise their sales strategies and respond to market trends more effectively.
- The solution employs the Frequent Pattern (FP) Growth algorithm, an advanced machine learning technique that identifies patterns and associations within transactional data. By analysing variables such as customer type, location, and other relevant attributes, the system generates insights to recommend optimal sales mark-up values.
- The project's aim was to create an automated system capable of predicting mark-up values that align with customer and location-specific trends, enabling data-driven decision-making and improving operational efficiency across the food and beverage sector.

The Hartree Centre Northern Ireland Hub provided invaluable support on our AI/ML project. Their expertise helped our team understand how to clean and prepare data, write Python code for machine learning, and how to train and test machine learning models. With this knowledge, we can now confidently integrate AI into CleverFoodie, empowering our customers to increase sales, reduce returns, and cut food waste in the food production and distribution sector.
Benefits
- The model demonstrated the ability to recommend personalised mark-up values for specific customer segments (e.g., Restaurant – ROI Rural) and products (e.g., CHICK FILLET 170-200GM). This suggests that the system can effectively adapt pricing strategies to different customer segments.
- By automating the analysis of complex, customer-specific transaction patterns, the system replaces the manual thresholding process previously used, significantly reducing time and human error. Tailored recommendations for customer segments and locations enhance pricing precision, resulting in more consistent, strategic, and competitive pricing decisions.
- Additionally, the system provides Aeroblue with a scalable edge, dynamically adapting to shifting market conditions and historical trends. This innovative approach empowers Aeroblue to make data-driven decisions, driving operational efficiency and strengthening their position in the food and beverage sector.
Further Information
This work was completed as one of our data projects. Our projects are up to 12 weeks in duration. We will work alongside your business to scope your project, demonstrate proof of concept, and explore options to deploy it within your business. If you would like to learn more about the Hartree NI Hub and our Programme, please get in touch with us at: info@hartreeni.uk
Our Impact on UK Industry and Society
The Hartree National Centre for Digital Innovation enables businesses to acquire the skills, knowledge and technical capability required to adopt digital technologies like super computing, data analytics, artificial intelligence (AI) and quantum computing. The SME Engagement Hubs play a key role in offering targeted support for business challenges in their region, creating value and generating economical and societal impact for the UK.

