Connected Tires
David Walter • 6 novembre 2025
Project Context
Michelin aimed to develop a new generation of digital services based on data from connected tires. The goal was to deliver added value to truck fleet managers and maintenance centers worldwide.
The challenge lay in the collection, processing, and supervision of massive sensor data, while ensuring robustness, scalability, and relevance of analytics.
Objectives
The main objective was to establish a technical and architectural framework to:
- Efficiently collect and process data from connected tires
- Monitor data flows and measure the impact of business use cases
- Create a Big Data platform ready to evolve with growing data volumes
Mission Duration
A long-term engagement, combining initial architectural study, implementation of monitoring systems, and ongoing support.
Implementation
Agaetis leveraged its Data and Cloud expertise to:
- Initial architecture study: Define technological and structural choices
- Platform monitoring: Implement monitoring principles to ensure robustness and availability
- Load analysis: Evaluate the impacts of various business use cases
- Big Data framework definition: Integrate best practices to accelerate implementation
- Data Science work: Perform domain-specific analysis and develop new indicators and services
Results Achieved
- New digital services: Creation of innovative solutions for fleet managers
- Robust and scalable platform: A Big Data environment ready to handle massive data volumes
- Operational optimization: Improved traceability and KPI tracking
- Enhanced innovation: Data transformed into strategic levers for Michelin
Key Success Factors
- Agaetis’ expertise in Big Data and IoT data processing
- End-to-end methodology: From architecture to Data Science
- Immersion in the client’s business environment
- Proactive supervision ensuring robustness and reliability
And You?
Are you wondering about:
- Leveraging data from your connected equipment?
- Creating new digital services based on Data?
- Implementing a robust and scalable Big Data architecture?
👉 Contact our experts to transform your IoT data into innovative, value-generating services.
Ressources Agaetis
Project Context France’s first research foundation dedicated to innovation in pain management aimed to launch a market-ready application resulting from its clinical research and development program. The goal was to transform the app into a Digital Therapeutic (DTx) reimbursed by the national health insurance system. Objectives The organization focuses on driving healthcare innovation through extensive collaborations with hospitals, research institutes, universities, and technology companies. The main challenges included: Transforming an application into a Digital Therapeutic (DTx) reimbursed by the national health system. Managing the transition of patients to this new platform. Preparing a new data warehouse to support scientific research. Mission Duration 3 collaborators over 3 years Methodology Agaetis provided its expertise through targeted and structured actions: Audit of existing systems: Evaluation of current infrastructure to identify needs and areas for improvement. Decision support for partner selection: Assistance in choosing competent and reliable technology partners. Technology advisory: Guidance on application architecture, security, and scalability to ensure long-term viability. Results Achieved Development of an application supporting patients in chronic pain management, progressing toward recognition as a reimbursable DTx. Rigorous technical assessment and selection of strategic partners. Adherence to roadmap milestones , ensuring steady progress and alignment with expectations. This project highlights how Agaetis leverages its technological and strategic expertise to transform challenges into innovative, effective solutions — creating tangible value for clients in the healthcare sector.
Context The topic of artificial intelligence is a major issue for an insurance and digital services group. The 2024 edition of a trade fair dedicated to local authorities, sponsored by the company, will focus on AI serving humanity . The company’s clients are already asking about its positioning regarding AI, and it aims to demonstrate its concrete commitment to this path. Proposal Support in developing an AI strategy and translating it into a 2- to 3-year roadmap , including the implementation of three proof-of-concept (POC) projects. Conclusion The POCs aim to demonstrate the practical capabilities of AI to improve support services, provide regulatory assistance, and facilitate multilingual communication. By assisting the company in defining and implementing its AI strategy, these POCs will showcase its commitment to innovation and continuous improvement of its services. 1. POC #1: Omnichannel Orchestrator for User Support Objective: Reduce employee workload and improve service quality. Solution: An omnichannel orchestrator that routes user requests to the best resolution channel. Technology Partner: A conversational orchestration solution provider. Timeline: 4 to 6 weeks. Budget: 12 to 15 joint workdays. Stakeholders: Support manager, customer relations expert, partner technical expert. 2. POC #2: AI Assistant for Urban Planning Objective: Provide additional regulatory assistance to municipal services. Solution: A chatbot based on a multi-source knowledge base, answering urban planning questions. Technology Partner: A specialized AI integrator. Timeline: 4 weeks. Budget: To be defined with partners. Stakeholders: Urban planning managers, AI experts, data scientists. 3. POC #3: Multilingual Chatbot for Local Authorities Objective: Facilitate communication between citizens and local administrations in multiple languages. Solution: A chatbot powered by public information from a website and other sources, supporting 3 to 4 languages. Technology Partner: A specialized AI integrator. Timeline: 4 weeks. Budget: To be defined with partners. Stakeholders: Web and business managers, AI experts, data scientists.
