AI enables scaling social inclusion systems with personalized care and early risk detection, strengthening the capacity of professionals to understand and act on complex social realities.
Strengthening the capacity of professionals to understand and act on complex social realities.
The integration of artificial intelligence into social inclusion systems is essential to address the growing complexity and scale of social challenges. Traditional approaches, however dedicated, face structural limits when confronted with multidimensional, evolving situations.
AI enables the integration and analysis of multiple dimensions (social, emotional, housing and community), offering a more complete and dynamic view of each situation.
It facilitates early risk detection, enabling action before situations deteriorate.
It makes it possible to offer responses tailored to each person, without losing system-wide scalability.
It reduces operational burden and provides decision-support tools, improving the quality of intervention.
It enables more efficient, coordinated and adaptive systems, capable of responding to changing contexts.
AI enables earlier intervention, scaling social action and generating real impact, reaching territories and people where care systems currently fall short.
Most AI systems are trained on internet-scale data and commercial datasets that do not reflect the realities of complex social systems. Critical dimensions of society remain invisible to artificial intelligence.
Residential trajectories marked by insecurity, profile affinity and the formalisation of cohabitation arrangements enable the anticipation of risks and the activation of more stable, suitable housing solutions.
Identifying poverty trajectories and social vulnerability patterns enables the definition of more precise and effective strategies to address them.
Community support networks can reactivate social ties and generate alternatives outside metropolitan areas, where housing pressure displaces the most vulnerable populations.
Depopulated territories can become spaces of welcome and opportunity, connected to stressed areas with underutilised services, yet this potential remains unidentified and unactivated.
The lack of stable housing directly impacts health and wellbeing, requiring integrated approaches between housing and socio-health care.
Digital literacy barriers and cognitive diversity require designing adapted interactions with professional support to ensure inclusive and effective use of AI.
Kloosiv.AI was born to close that gap — building a new social understanding infrastructure based on real data, professional knowledge and models designed specifically for complex social systems.
Artificial intelligence cannot work in complex social systems without the right foundations.
Housing instability, loneliness, migration, health and community dynamics generate fragmented and evolving data that traditional systems cannot structure. Before applying AI, the infrastructure to capture and understand these realities must exist.
Kloosiv built that infrastructure.
Over several years, we developed a socio-residential platform designed to structure social trajectories over time — integrating housing conditions, wellbeing indicators, community networks and vulnerability factors into a unified system.
Rather than applying generic AI models to incomplete datasets, Kloosiv focuses on building the conditions necessary for meaningful AI in social systems.
Our AI does not come from a lab, but from real social intervention. We built it alongside professionals, understanding how they act, decide and support.
That is why we do not impose technology: AI adapts to professional practice, because it was created from intervention upward.
Most AI systems rely on static datasets, simulations or digitally proficient users.
Kloosiv operates through living labs in communities, working with people in vulnerable situations and with barriers to accessing technology.
This enables the generation of structured data from cases, interventions and AI usage in complex contexts.
To make this possible, we have developed specific data collection and interpretation protocols, together with a specialized team combining technology and social intervention professionals.
This creates a continuous loop between practice, data and technology.
Kloosiv does not just develop AI — it operates as a living lab that enables continuous measurement, evaluation and intervention, making it possible to audit, iterate and improve AI systems in real-world contexts.
Without it, inclusion systems will remain reactive, limited in scope and difficult to sustain. Kloosiv is building the foundation these systems need to evolve.
Want to know how our research can be applied to your context?
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