DHDI builds climate‑nutrition AI for food‑insecure women and families in low‑resource, climate‑impacted regions. It combines a 26‑session participatory curriculum, Climate Health Photovox, that teaches climate and nutrition literacy while collecting consent‑based, low‑bandwidth data, with the EpiNu AI engine that generates food‑pairing recommendations from a proprietary micronutrient database, delivering actionable health guidance to community health workers and NGOs.
Funding
Funding not disclosed
Founders
Product
Problem
Poverty‑driven food and health insecurity is intensified by climate change, yet the most affected communities lack reliable data and tailored guidance because existing AI tools require stable connectivity, comprehensive datasets, and are not designed for low‑resource settings.
Solution
DHDI develops climate‑nutrition AI that is built directly with and for food‑insecure women and families living in climate‑vulnerable regions. Through a participatory curriculum called Climate Health Photovox, community members gain climate literacy, smartphone skills, and nutrition knowledge while generating ethically sourced, consent‑based data on local dietary shifts. This data feeds the EpiNu AI platform, which creates food‑pairing recommendations using a novel micronutrient database specifically curated for low‑resource contexts. The combined approach delivers actionable health guidance, improves nutrition outcomes, and continuously refines the AI model as more community data are collected.
Target Audience
Primary users are community health workers, women’s nutrition networks, and NGOs operating in low‑resource, climate‑impacted regions that serve food‑insecure households.
Features
- 26‑session Climate Health Photovox curriculum that blends climate, nutrition, and digital skills training for women in food‑insecure households
- Consent‑embedded data collection workflow that captures images and observations of climate‑induced food system changes
- EpiNu AI engine built on a proprietary micronutrient database tailored to the dietary realities of low‑resource populations
- Food‑pairing recommendations optimized for micronutrient adequacy and local availability, usable by community health teams without internet access
- Continuous development loop where community‑generated insights improve AI models, which in turn enhance community interventions
- Low‑bandwidth, mobile‑first design enabling deployment on basic smartphones in areas with limited connectivity