Updated on 11 July 2025
AI-Driven Circular Fashion Digital Paltform: MODA App
About
At ModaApp, we aim to build a new digital platform that matches people with similar body profiles using advanced AI-powered algorithms. This platform will enable users to sell, rent, or exchange clothes that genuinely fit—reducing returns and promoting a more personal, circular, and sustainable fashion experience.
Key Features
- Body-Based Matching Algorithm: Users are matched based on measurements to ensure fit, reducing return rates and waste.
- Reuse-First User Journey: All listings are second-hand. Reuse is the core model, not a side feature.
- Mobile-First, Consumer-Centric: Designed for intuitive reuse and traceability for everyday users.
- Optional DPP Data Integration: Ability to display DPP tags and metadata when available, creating digital transparency.
Stage
- Early idea
Topic
- Data technologies | (AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use paths
- Data technologies | Algorithm that shows the (positive) impact of a Circular Economy process or Circular Economy product
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Enabling technologies | Network design of reverse supply chains
Type
- Consortium seeks Partners
Attached files
Organisation
Similar opportunities
Project cooperation
- Early idea
- Already defined
- Consortium seeks Partners
- Enabling technologies | Robotic / handling - and assistance systems
- Enabling technologies | (Advanced) Materials and additive manufacturing
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Enabling technologies | Reverse Manufacturing (e.g. adaptive automation for high variance, sorting, sophisticated logistic systems)
- Data technologies | (AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use paths
Athina Kotrozou
Student
Stuttgart, Germany
Project cooperation
- Already defined
- Consortium seeks Partners
- Data technologies | Design of an adaptable Digital Product Pass:
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Enabling technologies | AI-driven diagnostic systems, e.g., for assessing the viability of reused, remanufactured, and recycled components
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
Diego Galar
Professor at Luleå University of Technology-Division of Operation and Maintenance Engineering
Lulea, Sweden
Project cooperation
Digital Product Passport for Circular Economy
- Already defined
- Expertise offered
- Consortium seeks Partners
- Data technologies | Assistance and Expert systems
- Data technologies | Design of an adaptable Digital Product Pass:
- Enabling technologies | (Advanced) Materials and additive manufacturing
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Data technologies | Algorithm that shows the (positive) impact of a Circular Economy process or Circular Economy product
- Enabling technologies | Industry 4.0 technologies (IoT, big data analytics) for monitoring and managing circular value chains
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Enabling technologies | AI-driven diagnostic systems, e.g., for assessing the viability of reused, remanufactured, and recycled components
- Data technologies | (AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use paths
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
- Enabling technologies | Manufacturing and machine learning, e.g., to increase the flexibility of industrial processes, modular approaches, reduce use of materials, quality assurance and certification of products)
Ralph Weissnegger
Director Innovation & Funding at CISC Semiconductor GmbH
Klagenfurt, Austria