Research & Development at Codete Enterprise Software House

Codete supports and executes innovation-driven digitalization across all types of business processes. A thorough consulting and support in overall digital transformation, by integrating data-driven solutions with the core business processes, resulting in a radical business digitalization lie at the heart of our offer. Codete's Research & Development team's experts in the fields of Machine Learning, Data Science and Big Data engineering lead your business into an era of innovation.

Research & Development Audit is the first phase of R&D projects – the challenges are identified and defined. In the course of an in-depth analysis, all the necessary data is collected, leading to a Proof of Concept demonstration of the suggested solution. Along the whole way, Codete's dedicated Research & Development teams make the best out of the contingent business subject-matter experts, who provide the necessary industry-specific insight and facilitate the communication between Codete Team and the Client, always kept in the loop due to full process transparency.


How it works

Business Audit

Codete R&D bespoke research consulting services are founded on a thorough audit, gathering all critical business information and highlighting optimization opportunities

Proof of Concept development

Codete Client-tailored innovations are embodied in a time-effectively developed PoC model solution, which demonstrates the impact the researched technology will have on Client's business.

MVP Implementation

Codete R&D team builds an essential Minimum Value Product, showcasing the core innovative value of the developed technology. Dedicated Codete IT engineers build it from scratch or serve as your auxiliary IT department in the process.

Success story


Being data-oriented is a trend for many companies to follow. Data is flowing everywhere and using it can bring great business advantages. It can be used for a variety of purposes. Almost every business process can be optimized, but to do so, you need to base the observation and measure the results with some kind of metrics. To produce those, you need to store and process the data to find meaningful patterns. Finding patterns and predicting results base on data gathered - that is exactly what machine learning was designed for.


DLConverter is a software which targets conversion of machine learning models with special emphasis on deep learning subset. It strives to be portable and secure by utilizing containerization principles via Docker and easy in day-to-day usage, yet pleasant for the eye of users. We have developed a set of Python libraries which are able to take Tensorflow models, process those and produce a model in Keras. This is exactly the same model, all of the results are the same, but we have managed to change a library used, without any need for further development or learning. That was exactly the result we wanted to achieve - with use of our converter we were able to translate a model from one framework to the other without any additional work. With converters like that, you can take all of the obsolete, legacy models your company uses and replace those with more modern ones, fitting current tech stack better. The same we can and want to do with other ML libraries and frameworks. Next targets would be MXNet to cover its R applications and integrate general solutions for models meeting ONNX standards which would cover multiple frameworks like PyTorch or Caffe. DLConverter is suited for distributed teamwork, e.g. each team can work on the specific technology-to-technology conversion. It’s due to custom and highly automated work environment developed specifically for this project. This workflow brings the opportunity of faster feature development and improves software reliability.