Proof of Usefulness Report

Blue AI

Analysis completed on 2/7/2026

+207
Proof of Usefulness Score
Gaining Momentum

Blue AI is a verifiable early-stage healthtech project (listed on MIT Solve 2023) addressing a high-value problem in Latin America: preventable hospitalizations and insurance fraud. The solution uses practical ML (XGBoost/NLP) with a no-code interface, showing strong potential utility ($20/mo vs $10k cost). However, the PoU score is heavily penalized by the low quality of the submission data, which contains hyperbolic claims ('everyone', 'most people have used my product') and vague financial metrics ('marketcap: 2500000'). While the underlying project is promising and technically sound, the lack of verifiable user traction metrics and the unprofessional submission limit the score to the 'Small but promising' range.

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Score Breakdown

Real World Utility+102.0
Audience Reach Impact+8.0
Technical Innovation+36.0
Evidence Of Traction+18.0
Market Timing Relevance+36.0
Functional Completeness+1.0
Subtotal+201
Usefulness Multiplierx1.03
Final Score+207

Project Details

Project URL
Description
Blue AI is an AI assistant that helps employers and healthcare providers in Latam catch fraud, misuse, and preventable hospitalizations from health plans, pointing out problems that businesses hardly see coming, can still be treated with $20 per month but might cost $10,000 per year, per member if not taken care of. Today, it's a no-code AI platform, where HR and Healthcare Managers drag and drop claims database files, and select pre-trained machine learning models, based on XGBoost and NLP mainly, without coding or statistical expertise. Finally, Blue AI provides a list of which employees might need care to prevent a hospitalization that might come in the next 12 months, 6x more precise than investing in the previous high-cost members (how it is done today), and points out unnecessary duplicated procedures, incorrect reimbursements, and other potential misuse. By doing so, Blue AI presents problems that can still be treated with $20 per month but might cost $10,000 per year per member if not taken care of, so employers can invest accurately in primary care up to 12 months in advance of something severe happening, at the same time leading to efficient and transparent use of health insurance and health plans.

Algorithm Insights

Market Position
Growing utility with room for optimization
User Engagement
Documented reach suggests active user community
Technical Stack
Modern tech stack aligned with sponsor technologies

Recommendations to Increase Usefulness Score

Document User Growth

Provide specific metrics on user acquisition and retention rates

Showcase Revenue Model

Detail sustainable monetization strategy and current revenue streams

Expand Evidence Base

Include testimonials, case studies, and third-party validation

Technical Roadmap

Share development milestones and feature completion timeline