{"id":4436,"date":"2025-07-10T09:36:19","date_gmt":"2025-07-10T07:36:19","guid":{"rendered":"https:\/\/fondazione-fair.it\/?p=4436"},"modified":"2026-04-24T13:07:11","modified_gmt":"2026-04-24T11:07:11","slug":"the-companies-featured-at-the-fair-event-on-july-14-and-their-projects","status":"publish","type":"post","link":"https:\/\/fondazione-fair.it\/en\/news\/the-companies-featured-at-the-fair-event-on-july-14-and-their-projects\/","title":{"rendered":"The companies featured at the FAIR event on July 14 and their projects"},"content":{"rendered":"
From applied research to business innovation: all the projects presented and shared throughout the day
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\n<\/strong><\/h6>\n

Milan, July 2025<\/em> \u2013 On July 14, as part of the event FAIR Ecosystem: AI between Research and Industry<\/strong> at the Politecnico di Milano<\/a>, companies, researchers, and institutions will come together to discuss the strategic role of artificial intelligence in driving national innovation.<\/p>\n

Throughout the day, projects and solutions developed within the FAIR Cascade Calls will be presented, involving companies, universities, and research centers in initiatives focused on technology transfer, collaboration, and competitive growth.<\/p>\n


\nBelow is the list of companies participating in the event and the projects they will showcase.
\n<\/strong><\/p>\n

Companies presenting on stage<\/strong><\/h5>\n

Four selected companies will present their projects on stage, sharing the challenges encountered in developing AI solutions and contributing to the discussion on Italian and European innovation pathways.<\/p>\n

ENI<\/strong> \u2013 Spoke 4
\nProject: Question-answering Enhancement via Systematic Large Language Models Fine-Tuning
\n<\/em><\/p>\n

At Eni, high-performance computing (HPC) is a cornerstone of the energy transition and AI-driven innovation. Within this context, the project \u201cQuestion-answering Enhancement via Systematic Large Language Models Fine-Tuning\u201d<\/strong> leverages computational power to refine large language models (LLMs) and develop tailored solutions for the company.<\/p>\n

The initiative involves adapting an LLM to Eni\u2019s internal regulatory corpus, with the aim of creating an intelligent Question & Answering<\/em> system. By combining Instruction Fine-Tuning<\/em> and Retrieval-Augmented Generation<\/em> techniques, the project has produced more compact, efficient, and high-performing customized models, optimizing the balance between accuracy and computational resources.<\/p>\n

The result is a language model aligned with Eni\u2019s technical lexicon and domain-specific terminology, capable of delivering precise answers on internal regulations and policies. Integration into corporate applications will enable faster and more efficient access to regulatory knowledge for employees.<\/p>\n


\nPlaynook<\/strong> \u2013 Spoke 2
\nProject: Adaptive AI for multi-path storylines generation and validation
\n<\/em><\/p>\n

PlayNook<\/strong> is an audio-first startup, founded in 2023 in Tuscany, specializing in the development and distribution of AudioGames<\/strong>\u2014immersive and interactive experiences based entirely on sound. The company aims to redefine gaming by using audio as the primary interaction medium.<\/p>\n

PlayNook leverages audio to create engaging and inclusive experiences. Its platform allows users to interact with games through voice commands, without the need for screens or controllers. With the ability to play anywhere, even offline, the experience becomes continuous and accessible in any context.<\/p>\n


\nReply<\/strong> \u2013 Spoke 7
\nProject: RAMEn \u2013 Robust AI for Multi-agent Environments<\/em><\/p>\n

RAMEn<\/strong> introduces an innovative system for semantic representation of 3D environments based on images and point clouds, aimed at improving collaboration among robots. These intelligent maps, shareable across multiple agents, support navigation, planning, and natural interaction. Applications include logistics, surveillance, and assistive robotics.<\/p>\n


\nOPTIT<\/strong> \u2013 Spoke 8
\nProject: MILA \u2013 Hybrid Modeling for Decision Support in Logistics and Environmental Contexts<\/em><\/p>\n

The MILA<\/strong> project, developed by Optit<\/strong> in collaboration with the University of Bologna within SPOKE 8<\/strong> of the FAIR programme, focuses on logistics, supply chain, and waste management.<\/p>\n

Building on Optit\u2019s expertise in symbolic decision-support models, the project explores the integration of data-driven<\/em> approaches such as machine learning and neural networks to enhance data collected from IoT systems and heterogeneous sources.<\/p>\n

The initiative has led to the prototyping of hybrid models and the development of new functionalities tested in real-world use cases. Results will be shared with the scientific community and integrated into Optit\u2019s platforms, strengthening the company\u2019s competitiveness in applied AI for logistics and environmental management.<\/p>\n

Companies showcasing their projects<\/strong><\/h5>\n

In addition to the stage presentations, the event will feature a dedicated exhibition area showcasing projects developed through the FAIR Cascade Calls, creating a space for dialogue, networking, and knowledge exchange.<\/p>\n

S&h Srl<\/strong> \u2013 Spoke 4 (POLIMI)
\nProject: MMOVIDA \u2013 Multimedia Models for Verification, Inspection and Defect Analysis<\/em><\/h6>\n

The MMOVIDA project develops software tools based on multimodal AI models to optimize quality inspection processes in industrial manufacturing. With over forty years of experience in industrial electronics and automation, S&h aims to create systems capable of detecting defects in complex devices even with limited and imbalanced training datasets, ensuring intuitive human\u2013machine interaction.<\/p>\n

Melixa Srl<\/strong> \u2013 Spoke 2 (FBK)
\nProject: AGRIBEE<\/em><\/h6>\n

AgriBee AI<\/strong> is an innovative project leveraging integrative AI to promote sustainability in beekeeping and agriculture. By combining historical data from the BEENET<\/em> network, Melixa sensors, and environmental datasets, the system generates predictive models on bee health and honey production.<\/p>\n

The goal is to provide actionable insights to beekeepers and farmers, supporting profitability, biodiversity, and ESG transition in the agri-food sector.<\/p>\n

Genius Loci<\/strong> \u2013 Spoke 2 (FBK)
\nProject: iGeSPi \u2013 Intelligent Geo-Spatial Pipeline<\/em><\/h6>\n

iGeSPi<\/strong> aims to revolutionize geospatial data analysis, making it accessible even to non-technical users through AI, machine learning, and natural language interfaces. The platform supports strategic decision-making in areas such as land management, real estate evaluation, and risk analysis.<\/p>\n

Deix<\/strong> \u2013 Spoke 2 (FBK)
\nProject: INTEGRA<\/em><\/h6>\n

INTEGRA<\/strong> develops advanced technologies for automated ergonomic risk assessment in workplaces, combining AI and human motion modeling. By analyzing images and videos, it detects posture and movement, supporting injury prevention and workplace safety.<\/p>\n

Spindox Spa<\/strong> \u2013 Spoke 4 (POLIMI)
\nProject: TLIALOC<\/em><\/h6>\n

TLIALOC<\/strong> is an AI-driven system for the diagnosis and personalized treatment of pulmonary embolism. By analyzing CT scans, it detects clots and assesses clinical severity, aiming to improve diagnostic accuracy and healthcare efficiency.<\/p>\n

FifthIngenium<\/strong> \u2013 Spoke 4 (POLIMI)
\nProject: MIXIR<\/em><\/h6>\n

MIXIR<\/strong> integrates AI with extended reality (XR) technologies to support industrial operations such as maintenance, assembly, and quality control through interactive and multimodal interfaces.<\/p>\n

3rdPlace<\/strong> \u2013 Spoke 8 (UNIBO)
\nProject: FINBUDDY<\/em><\/h6>\n

FINBUDDY<\/strong> promotes financial literacy through AI, offering accessible and personalized financial information to support informed decision-making for individuals and organizations.<\/p>\n

DeepTrace Technologies<\/strong> \u2013 Spoke 4 (POLIMI)
\nProject: Trace4AFEM<\/em><\/h6>\n

Trace4AFEM<\/strong> develops a federated AI platform for personalized medicine, enabling secure and distributed model training across healthcare institutions while preserving patient privacy.<\/p>\n

Agricola Moderna<\/strong> \u2013 Spoke 4 (POLIMI)
\nProject:
\nComing soon.<\/h6>\n
Biocubica srl<\/strong> \u2013 Spoke 4 (POLIMI)
\nProject: SoundIA \u2013 The Future of Health through Continuous Monitoring<\/em><\/h6>\n

SoundIA<\/strong> develops wearable technologies for continuous monitoring of vital and environmental parameters, enabling early detection of health risks and personalized interventions.<\/p>\n

Kiwifarm<\/strong> \u2013 Spoke 7 (POLITO)
\nProject: ARGORIS<\/em><\/h6>\n

ARGORIS<\/strong> is an intelligent platform for optimizing logistics fleet management, improving efficiency and sustainability through advanced algorithms.<\/p>\n

MyMeta Srl<\/strong> \u2013 Spoke 2 (FBK)
\nProject:
\nComing soon.<\/h6>\n

 <\/p>\n

The July 14 event represents a key opportunity for dialogue between academia and industry, fostering a collaborative ecosystem for artificial intelligence innovation in Italy.<\/p>\n

View the final programme here<\/a>.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

From applied research to business innovation: all the projects presented and shared throughout the day Milan, July 2025 \u2013 On July 14, as part of the event FAIR Ecosystem: AI between Research and Industry at the Politecnico di Milano, companies, researchers, and institutions will come together to discuss the strategic role of artificial intelligence in…<\/p>\n","protected":false},"author":1,"featured_media":3435,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[46],"tags":[151,150,154,153,152,38,149],"class_list":["post-4436","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-ai","tag-artificial-intelligence","tag-fair-poject","tag-fair-project","tag-ia","tag-intelligenza-artificiale","tag-pnrr"],"_links":{"self":[{"href":"https:\/\/fondazione-fair.it\/en\/wp-json\/wp\/v2\/posts\/4436","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fondazione-fair.it\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fondazione-fair.it\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fondazione-fair.it\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/fondazione-fair.it\/en\/wp-json\/wp\/v2\/comments?post=4436"}],"version-history":[{"count":2,"href":"https:\/\/fondazione-fair.it\/en\/wp-json\/wp\/v2\/posts\/4436\/revisions"}],"predecessor-version":[{"id":4438,"href":"https:\/\/fondazione-fair.it\/en\/wp-json\/wp\/v2\/posts\/4436\/revisions\/4438"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/fondazione-fair.it\/en\/wp-json\/wp\/v2\/media\/3435"}],"wp:attachment":[{"href":"https:\/\/fondazione-fair.it\/en\/wp-json\/wp\/v2\/media?parent=4436"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fondazione-fair.it\/en\/wp-json\/wp\/v2\/categories?post=4436"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fondazione-fair.it\/en\/wp-json\/wp\/v2\/tags?post=4436"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}