Helix

Inspiration

Most health platforms treat genetics, nutrition, movement, sleep, and preventive care as separate problems. We created Helix to connect them. Our goal is to make genomics research understandable and actionable, so people can identify potential health risks earlier and build healthier habits before preventable conditions become harder to manage.

What it does

Helix combines genomics, AI, biomarker tracking, and lifestyle data to create personalized preventive wellness guidance. Users can explore genetic risk insights, receive recommendations for food, exercise, sleep, and checkups, track changes over time, and prepare useful questions for healthcare professionals. The platform considers personal goals, cultural food preferences, and time constraints to make each recommendation practical.

How we built it

We built Helix as a full-stack web application using React, TypeScript, Python, FastAPI, and a Supabase-ready data architecture. We designed a genomics processing pipeline, personalized recommendation logic, wellness check-ins, biomarker timelines, privacy controls, and an interactive AI coach experience. We also created a sample profile with genetic insights, health measurements, and lifestyle history for a complete product walkthrough.

Challenges we ran into

Our biggest challenge was translating complex genomics research into guidance that feels clear without presenting uncertain information as a diagnosis. We also had to connect many different types of health data while keeping the experience simple, useful, and privacy-conscious. Building a polished product with meaningful personalization within a short hackathon timeline required careful prioritization.

Accomplishments that we’re proud of

We are proud that Helix turns a technically complex idea into an approachable preventive wellness experience. The prototype connects genetic risk, nutrition, physical activity, sleep, biomarkers, progress tracking, and professional support in one platform. We also built adaptive wellness plans that respond to user check-ins instead of producing generic, one-time recommendations.

What we learned

We learned that personalized health technology must explain both what the data suggests and what the data cannot determine. Genomics becomes more valuable when it is combined with lifestyle context, ongoing measurements, and professional guidance. We also learned that the most effective recommendation is often a small, realistic action that a person can sustain.

What’s next for Helix

Next, we plan to integrate a live AI model, validated polygenic risk models, secure genomic data processing, and wearable health data. We also want to collaborate with researchers and healthcare professionals to evaluate the platform, expand its evidence base, and connect users with dietitians, physical therapists, genetic counselors, and preventive care providers. Our long-term goal is to make personalized disease prevention more accessible, understandable, and actionable.

Built With

• go
• replit
• superhuman