MataConnect: AI-Powered Discovery for Women’s Communities

Making women's support networks discoverable through AI, because connection shouldn't be left to chance.

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Inspiration

Through a survey I conducted with 500 women across the world, 95% said they struggle to find the right communities, 90% found life-changing groups purely by chance and 88% of community founders can't reach the people they're trying to serve. As a woman in tech, I’ve felt that disconnection firsthand. And one thing became clear: Thousands of empowering communities are still invisible to the women who need them most.

That is the problem MataConnect solves. It helps women discover the right communities with intention, not luck, using AI to make support more discoverable and accessible.

It is a mission to make sure every woman can find the power she needs to thrive.

What it does

MataConnect is an AI-powered discovery engine that helps women find communities with intention, not luck. Users enter natural language queries like "Tech communities for women in Kenya," and our platform uses semantic search to return perfectly matched communities with full details.

How it was built

  • Frontend: React/Next.js for intuitive user experience
  • Backend: FastAPI with Python
  • AI/ML: Google Vertex AI's Gemini model for semantic embeddings
  • Database: MongoDB Atlas for vector search capabilities
  • Data Pipeline: Automated sourcing, cleaning, and embedding of community data
  • Data Source: Real community data from US Data.gov
  • Deployment: Containerization with Docker on Google Cloud Run, Both frontend and backend are containerized with Docker to facilitate easy deployment and scalability.

Below is a diagram of how the AI search works AI Search

Challenges faced

  • Preventing bias in recommendations: Ensuring our AI doesn't inadvertently favour certain types of communities over others
  • Handling ambiguous queries: Users often search with vague terms like "women in tech" - making our AI smart enough
  • Handling community lifecycle changes: Managing communities that become inactive, merge with others, or evolve their focus over time
  • Privacy-first data collection: Gathering comprehensive community information while respecting privacy regulations and user consent across different jurisdictions

Important accomplishments

  • Successfully implemented end-to-end AI-powered semantic search
  • Built a scalable data pipeline processing real world communities data
  • Created an intuitive interface: through feedback from beta testers, the UI is visually appealing, functional, and accessible
  • Validated the problem with 500+ women globally before building

Lessons learned

  • The power of semantic search over traditional keyword matching for community discovery
  • Vector databases can deliver lightning-fast, relevant results at scale

What's next for MataConnect

  • Global scale: Integrate data from around the world, starting with full UK coverage and expanding across regions
  • Smarter personalization: Learn from user preferences to deliver better, more relevant community recommendations
  • User accounts: Let users create profiles, save communities, and manage their discovery experience

In Conclusion

MataConnect is more than a search platform; it’s a bridge connecting women to the communities built to support them. Through AI and curated data, connection becomes intentional and not accidental.

The goal is to build a platform that is intelligent, inclusive, and scalable. As the ecosystem expands, the mission remains clear: To make empowerment discoverable for every woman, everywhere.

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