AI Development for Public Health  

How Gooey.AI works with Healthcare Institutions

Gooey.AI partners with health organizations to help teams and institutions thoughtfully integrate AI into healthcare delivery and implementation. We recognize that health systems and health-care organizations today face limited frontline capacity and resources, lack of timely access to healthcare services, and significant privacy concerns.

We work with health organizations that want a legitimate AI partner, one that understands and shares their concerns around privacy, responsibility, and real-world deployment. Health teams choose to work with us because patient data is never used to train models, and AI solutions are built with the utmost privacy in mind.

Gooey.AI collaborates with respected funders including the Wellcome Trust, the Rockefeller Foundation, and the Gates Foundation. Our platform supports zero data retention, data within jurisdiction, and on-prem deployment options. We provide an accessible, collaborative platform that connects easily to commonly used channels such as WhatsApp and voice, and is well suited for multilingual contexts.

In addition, Gooey.AI allows rigorous evaluation, enabling health organizations to compare the latest fine-tuned models fairly. We help teams understand shared patterns with other organisations, for example how conversational health data can be translated into structured clinical assessments, such as PHQ-9.

Gooey.AI agents offer the following features:

-Transcription for medical conversations via WhatsApp voice notes and saving to your private environment
-Support for virtually all languages by aggregating all major private and public ASR, translation, and text to speech models
-Turning unstructured conversations into structured medical records based on an orgs particular schema, protocol and internal systems
-Secure lookups and writing records to external DBs
-Categorization of usage and analyze trends through conversational queries and dashboards
-Hashing PII data so external servers never interact directly with sensitive data

Guided by our vision of meaningful impact, we support health organizations in building and deploying scalable solutions that advance patient health outcomes, empower frontline workers and leave no one behind.
Case Studies

"The Gooey team has been great and has amazing vibes. With their help, we went from an idea (a multi-lingual AI WhatsApp bot to help migrants find health clinics) to field tests in 12 weeks with one team member."

Shadi Henchiri

Senior Data and Research Associate at UN-IOM Migration
LinkedIn
Partnered with us

Healthcare's Theory of Change

Case Studies - Embracing Participatory and Inclusive Processes

Case study

Afiya: AI-Powered Health Guidance for Migrants in Tunisia

The UN IOM office in Tunisia serves 20,000 migrants from across Africa navigating migration to Europe. Migrants often face barriers to timely and accurate health information, especially when dealing with complex health systems and with language constraints.

To address this, UN IOM Tunisia partnered with Gooey.AI to build Afiya, a WhatsApp-based AI health assistant. Field-tested with 120 users, Afiya draws on structured health guidance to deliver real-time, multilingual support and care pathway guidance. Refined with migrant feedback, the tool helps users access essential health services safely and efficiently, improving health outcomes and promoting informed decision-making.

Case study

Accelerating Generative AI for Mental Health Research with MEXA

Gooey.AI partnered with Wellcome Trust, NeuroMatch and MEXA for the Generative AI for Mental Health Accelerator, to support early-stage teams building innovative, real-world AI solutions. The MEXA Accelerator was a four-month initiative created to equip interdisciplinary research teams with the tools, mentorship, and strategic support.

Through targeted technical workshops and hands-on clinics, we helped teams strengthen their generative AI applications. By working closely with the cohort, we enabled teams to move from prototype to deployment-ready AI systems, ensuring their AI solutions were scalable, impactful, and grounded in real user needs.

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