ALOS
(2026)
A digital peer-to-peer platform designed for caregivers of elderly with Alzheimer’s and other forms of dementia. Built through continuous dialogue with real caregivers, the platform directly addresses the isolation that follows a diagnosis and provides support for daily struggles. At the core of the system is an AI-powered matchmaking engine that connects caregivers seeking advice with peers who have navigated the same challenge.
4 million people in Italy alone care for elderly with Alzheimer’s and other forms of dementia. However, following a diagnosis, family caregivers often navigate medical care, bureaucracy, and daily life entirely alone.
To understand caregivers' core needs and their attitudes toward AI, we conducted a four-phase user research. The findings reveal caregivers feel unsupported by healthcare systems and urgently seek peer support networks. AI is welcomed if it helps building a caregivers' network, but data privacy has to be guaranteed.
So, how might we connect caregivers with the right peer knowledge when they need it, to reduce isolation? The Solution: Alos. An AI-driven service connecting caregivers facing similar challenges. Alos reduces isolation through smart peer matching, a private diary, community data insights, and direct links to local associations.
At the core of the service is the Alos chatbot. When an issue arises, caregivers describe it in their own words, and the AI suggests three peer profiles who navigated the same challenge, transitioning directly into human-to-human conversations.
The diary serves as a private, emotionally safe space to log daily routines and reflections, mapping emotional patterns over time to continuously refine personalized recommendations.
Chats are a private 1-to-1 channel designed to foster trust, emotional exchange, and practical advice between matched peers. Follow-up feedback prompts assess the match’s value, enriching the platform’s solution database to improve future AI matches.
Caregivers earn badges for supporting peers, valuing both offering and seeking help while highlighting practical caregiving expertise across the community.
To address the lack of local resources, this directory maps nearby caregiver associations, care facilities, and community initiatives for accessible on-the-ground support.
To foster a sense of community, a dashboard displays collective data, shared activity, and community milestones.
The onboarding flow captures three key inputs: the caregiver's workload and emotional baseline, the care recipient's dementia stage and behaviors, and the user's primary motivation to frame privacy boundaries transparently.
Prototyping phase was made of three phases, each informed by the testing's results of the previous one. The tests were conducted with caregivers to gain the most important insights, supported by peer testings and expert reviews sessions.
The AI model to find the best caregiver matches is RAG-Powered Recommender System. Semantic search quickly narrows candidates by lived experience, while an LLM delivers the final judgment on match quality.
Key learnings
Designing transparent AI-to-human handoffs;
Mixed-method UX research & synthesis;
Advanced component architecture & Figma prototyping;
Moderated usability testing & user facilitation;
Design system consistency & accessibility principles;
Additional credits
Diletta B. Canè, Cecilia Ferrentino, Agnese Pozzi, Vittoria Volontè