Solutions

The training of your neural networks is based on a training database. The performance of the network will depend on the quality of this database. It is not enough to check its quality by statistical methods, it is also necessary to understand how the neural network will learn against this database. Saimple helps you to check what a network has learned and how to improve its learning. Already in the upstream phase you have a better control of the learning that is done and you save time in building a solid learning base.>

Choosing a neural network architecture has a major impact on the performance. In order to have good performance it is necessary to optimize any chosen architecture to the task at hand. This is a complex and time-consuming task with no guarantee of success once you move into production. Being able to control step by step the influence of any architecture on your system performance will help optimizing it more quickly and with greater chance of success.

Moving a neural network in production is no easy task. Performance and reliability must be accounted for, as well as proper documentation for regulatory and standardization bodies when necessary. Today validation of neural networks cannot be only about testing, it also has to be about proving. Once the design and training phases are done proper validation takes place. With the Saimple tool you can easily set-up, roll out and document your validation plan for every stakeholder you have.

Our solutions and skills can be tailored to your needs and your organization. Our tools are available on license, through a server using our multi-OS client, or in SaaS. We also provide services such as auditing your AI' making process, training your workforce, providing support and maintenance on our tools.