matchHer

Inspiration

Inspired by other "Big-Little" programs, we wanted to provide a convenient way for women at any stage of their career to find a career mentor. We wanted to foster long-lasting, meaningful mentorship relationships with our users.

What it does

Our project uses AI to connect mentors to mentees. The mentee fills out a short survey to explain their background, industry, experience level, and goals and we use AI to process these responses and match mentees with a mentor. After getting connected, mentees can schedule meetings with their mentors, write notes about their meetings, and review notes from previous meetings.

How we built it

For frontend, we used Next.js, Tailwind, and Typescript. For backend, we used Supabase. For our vector database we used Pinecone. Finally, we used the Open AI API for embedding generation.

Challenges we ran into

We ran into many issues with our front-end. It was difficult to make all the pages link up correctly, and we ran into issues integrating our front-end with our back-end. We also ran into many issues deploying our platform: we ran into errors with unused components and project structure.

Accomplishments that we're proud of

We were able to deploy a full stack solution on Vercel. We used multiple API within our projects.

What we learned

We learned about a lot about vector databases and NLP.

What's next for Mentor-Her

We want to add a Resume Scanner into our survey to improve our matching algorithm. With more information, we can facilitate stronger connections. We also would like to add a Ice Breaker generator to help with the beginning of mentorship relationships. Finally, we would l

Built With

• next.js
• openai
• pinecone
• supabase
• tailwind
• typescript