WebRatio and RES IT, companies of RES Group, are pleased to announce that the innovative project ALGO has been selected to participate in Fortissimo Plus, a European Union initiative aimed at promoting the adoption of HPC (High-Performance Computing) and AI (Artificial Intelligence) technologies among SMEs and startups.
ALGO: a new step forward in low-code development
The ALGO project, launched in January 2025, supports the evolution of the WebRatio low-code platform through integration with Large Language Models (LLMs). This is made possible through the partnership with RES IT and its extensive experience in HPC, LLM and AI, developed within the Data Valorization competence center.
ALGO combines the complementary expertise of the two RES Group companies, enabling an efficient and focused approach to accelerating technological development in the low-code domain. In addition to representing a first step in WebRatio’s long-term development roadmap, ALGO aims to deliver tangible benefits to the European technology landscape.
A project selected to drive innovation
ALGO has been selected as one of 18 European Innovation Studies projects within Fortissimo Plus (FFplus), under the theme “Development of Generative AI Models.”
FFplus is a European initiative designed to promote the adoption of High-Performance Computing (HPC) among SMEs and startups, facilitating access to advanced technologies to improve competitiveness, innovation and sustainability. Active since 2024, it funds business experiments and innovation studies through six Open Calls, providing financial and technical support for the adoption of HPC, generative AI and digital simulation.
The objective is to help European companies optimize processes, reduce costs and develop innovative solutions based on artificial intelligence and LLMs across sectors such as healthcare, ICT, media and engineering.
In addition to the support provided by FFplus, ALGO also benefits from funding from another major European initiative, EuroHPC, which provides the computational resources required to optimize the performance of the AI model.