Nexyad implements published methodologies, such as for example the AGENDA methodology (General approach to neural studies for the development of applications) in particular for hybrid systems integrating both classic algorithms and deep learning. These methodologies allow traceability of solutions, promote updating and maintenance. We mainly deal with classification problems, reinforcement learning, decision making and generative AI. We also use AI to adjust the parameters of classic models, which guarantees maximum explainability.
Our experienced team promotes the use of explainable AI whenever possible and sets up consistency control systems to promote the regularity of solutions. We analyze the learning bases rigorously and know how to develop them iteratively. We use an AI technology, a collection of tested and validated objects of 150,000 lines of C++ code. In addition to this AI factory, the best algorithms and rapid prototyping environments (python, tensorflow, GPT chat, etc…) Nexyad manages the addition of features without starting the learning from scratch.
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