Hi Meta FAIR Licensing Team,
My name is Enrique Lourenço. I'm the founder of MedContent AI
(medcontent.ai), a SaaS platform deployed in Brazil that helps
medical professionals create educational health content that
genuinely engages patients.
The problem we're solving
Cardiovascular disease is Brazil's #1 cause of death. Despite this,
patient education content created by cardiologists and surgeons
rarely achieves sustained attention — patients scroll past, ignore,
or fail to retain critical health information.
The challenge isn't lack of medical knowledge. It's that doctors
have no way to understand how a patient's brain actually processes
their content before publishing it.
How we use TRIBE v2
MedContent AI integrates TRIBE v2 to analyze educational health
content — videos, carousels, and visual posts — frame by frame.
Specifically:
Medical professionals upload short-form educational content
TRIBE v2 runs inference to identify neural activation patterns:
which frames trigger genuine attention, which cause cognitive
overload, and where the audience neurologically disengages
Output: actionable editing recommendations that help doctors
create content that patients actually process and retain
Real-world validation:
We are currently working with Dr. Domingos Lourenço, a
cardiovascular surgeon with 32 years of experience at InCor
(Instituto do Coração — Brazil's leading cardiology institute,
São Paulo). Dr. Lourenço creates educational Instagram content
about cardiac conditions for patients. TRIBE v2 analysis has
directly informed which slides in his educational carousels
activate attention vs. generate cognitive load — improving the
reach and retention of cardiovascular health education.
Platform status
Platform: MedContent AI (https://medcontentai.com.br/)) — deployed
Stage: Early validation with first paying users
Our request
We would like to inquire about:
A commercial license or alternative arrangement permitting
use of TRIBE v2 model weights within MedContent AI as a
paid service for medical professionals
Any attribution requirements Meta FAIR would require in
user-facing outputs, documentation, or publications
Whether a research partnership or academic collaboration
model would be more appropriate given our use case in
health education
Revenue sharing terms, if applicable
We are fully committed to:
Proper attribution of TRIBE v2 and Meta FAIR in all outputs
Not redistributing model weights
Sharing anonymized engagement data with Meta FAIR for
research purposes if useful
Publishing findings on neural engagement in health education
content in collaboration with Meta FAIR researchers
Why this matters
Brazil has ~214 million people and a significant shortage of
effective health communication. Cardiologists like Dr. Domingos
are trying to educate patients about life-saving information —
valve disease, arrhythmia, hypertension — but have no objective
way to know if their content is neurologically effective.
TRIBE v2 is the only tool that can answer that question at scale.
The potential public health impact of applying brain-predictive
modeling to medical education in a country of 214 million people
is substantial — and aligns directly with Meta FAIR's mission of
advancing science for human benefit.
Thank you for open-sourcing this remarkable work. We would welcome
any guidance on the appropriate path toward commercial use.
Best regards,
Enrique Lourenço
Founder, MedContent AI
Email: enriquefdias22@gmail.com
LinkedIn: (https://www.linkedin.com/in/enrique-lourenco/)
Platform: https://medcontentai.com.br/
Location: São Paulo, Brazil
Market: Brazilian licensed physicians (~500,000 doctors)
Focus: health education, surgical education,
preventive medicine communication
Infrastructure: Claude API (Anthropic) + Supabase + React
Hi Meta FAIR Licensing Team,
My name is Enrique Lourenço. I'm the founder of MedContent AI
(medcontent.ai), a SaaS platform deployed in Brazil that helps
medical professionals create educational health content that
genuinely engages patients.
The problem we're solving
Cardiovascular disease is Brazil's #1 cause of death. Despite this,
patient education content created by cardiologists and surgeons
rarely achieves sustained attention — patients scroll past, ignore,
or fail to retain critical health information.
The challenge isn't lack of medical knowledge. It's that doctors
have no way to understand how a patient's brain actually processes
their content before publishing it.
How we use TRIBE v2
MedContent AI integrates TRIBE v2 to analyze educational health
content — videos, carousels, and visual posts — frame by frame.
Specifically:
Medical professionals upload short-form educational content
TRIBE v2 runs inference to identify neural activation patterns:
which frames trigger genuine attention, which cause cognitive
overload, and where the audience neurologically disengages
Output: actionable editing recommendations that help doctors
create content that patients actually process and retain
Real-world validation:
We are currently working with Dr. Domingos Lourenço, a
cardiovascular surgeon with 32 years of experience at InCor
(Instituto do Coração — Brazil's leading cardiology institute,
São Paulo). Dr. Lourenço creates educational Instagram content
about cardiac conditions for patients. TRIBE v2 analysis has
directly informed which slides in his educational carousels
activate attention vs. generate cognitive load — improving the
reach and retention of cardiovascular health education.
Platform status
Platform: MedContent AI (https://medcontentai.com.br/)) — deployed
Stage: Early validation with first paying users
Our request
We would like to inquire about:
A commercial license or alternative arrangement permitting
use of TRIBE v2 model weights within MedContent AI as a
paid service for medical professionals
Any attribution requirements Meta FAIR would require in
user-facing outputs, documentation, or publications
Whether a research partnership or academic collaboration
model would be more appropriate given our use case in
health education
Revenue sharing terms, if applicable
We are fully committed to:
Proper attribution of TRIBE v2 and Meta FAIR in all outputs
Not redistributing model weights
Sharing anonymized engagement data with Meta FAIR for
research purposes if useful
Publishing findings on neural engagement in health education
content in collaboration with Meta FAIR researchers
Why this matters
Brazil has ~214 million people and a significant shortage of
effective health communication. Cardiologists like Dr. Domingos
are trying to educate patients about life-saving information —
valve disease, arrhythmia, hypertension — but have no objective
way to know if their content is neurologically effective.
TRIBE v2 is the only tool that can answer that question at scale.
The potential public health impact of applying brain-predictive
modeling to medical education in a country of 214 million people
is substantial — and aligns directly with Meta FAIR's mission of
advancing science for human benefit.
Thank you for open-sourcing this remarkable work. We would welcome
any guidance on the appropriate path toward commercial use.
Best regards,
Enrique Lourenço
Founder, MedContent AI
Email: enriquefdias22@gmail.com
LinkedIn: (https://www.linkedin.com/in/enrique-lourenco/)
Platform: https://medcontentai.com.br/
Location: São Paulo, Brazil
Market: Brazilian licensed physicians (~500,000 doctors)
Focus: health education, surgical education,
preventive medicine communication
Infrastructure: Claude API (Anthropic) + Supabase + React