Development services for machine learning and AI

Machine learning enables large amounts of data to be analysed and processed quickly and accurately. Quimron applies technological expertise in machine learning and artificial intelligence development to support customers at scale. Our R&D capabilities in rapid prototyping and customised machine learning solutions enable us to enter unseen market segments, become more efficient and deliver measurable business results.


Machine learning and AI development

In recent years, great strides have been made, especially in the field of machine learning, and the market is demanding customised AI solutions for narrow requirements. The adoption of AI goes hand in hand with the adoption of other technologies. The integration of AI with IoT is expected to bring new opportunities for global businesses. Despite fears that AI will steal today's jobs, humans and machines should work together as a team to unleash the full potential of this technology. AI augmentation of human technical skills is expected to become key to the success of AI-integrated solutions.

Predictive Maintenance

The use of predictive maintenance tools brings benefits from the outset by providing visibility and control over processes. As the pool of available data is enriched, analysis is expanded, improving completeness and accuracy in predicting performance and potential events.

Integration of AI

The incorporation of AI is already part of our lives and we need to incorporate it into our organisations to improve our competitiveness and our ability to meet current and future challenges with confidence.


A reliable demand forecast is a powerful tool for optimising a company's logistics structure, and a proper logistics consultancy project is the best way to find the optimal solution for your demand forecast. Every case is different, but the benefits are the same.

Start with the right AI solution for you

The most common AI services

AI in manufacturing is essential to optimise supply chains. Machine learning technology enables companies to perform tasks on a gigantic scale, which was previously unimaginable. In short, it optimises a wide range of aspects. The machine learning integration process involves applying algorithms to your data to perform a task or solve a problem. To do this, the results need to be clearly defined. When you complete your AI implementation, you should use a mix of machine learning models and integrated business outcome visualisation tools. To successfully deploy machine learning systems in the network, specific requirements are needed.

Results of the AI assumption

How is artificial intelligence used in practice? After implementing AI and machine learning to optimise defined business functions, the benefits of AI may need to be evaluated. One benefit common to all companies will be the automation of processes through better processing of large volumes of data. Other business processes may vary by industry. Cloud solutions can be used to run workloads from anywhere. In short, data can be connected at any time and used without location restrictions. Data queries can be performed across multiple sources on centralised and unified platforms.