Dear Participants,

Congratulations to all the winners of the CyborgDB Hackathon 2025! 🎉Your innovation, effort, and technical excellence truly stood out, and we’re proud to celebrate your achievements.

🏆 Winners Announcement

Grand Prize ($3,500)Yash Arote – FALCONProject: FALCON

Best Healthcare Solution ($1,500)Mihir Phalke – RareNetProject: RareNet

Best FinTech Solution ($1,500)algposc – Alpha-GuardProject: Alpha-Guard

Best Enterprise Solution ($1,000)EchoSynth – SynthraxProject: Synthrax

Best Technical Feedback ($1,000)Gokul Krishna R – MedsecProject: Medsec

A big thank you to all participants for making this hackathon a success. We were truly impressed by the quality of ideas and solutions submitted.

Once again, congratulations to the winners!

OVERVIEW

Submit your feedback today: https://github.com/cyborginc/cyborgdb-community-feedback

Just your honest insights on the product.

Don't miss this chance to contribute and earn!

Build AI Solutions Impervious To Vector Inversion Using CyborgDB’s Encrypted Vector Search

Vector databases are the backbone of modern AI — powering everything from RAG systems and semantic search to fraud detection. But with great power comes a huge risk: embeddings are fully invertible.

That means if a database is breached, the original data can be reconstructed — from sensitive medical records and financial transactions to proprietary code.

In this hackathon, we’re challenging innovators like you to build AI applications that handle sensitive data without the vulnerability to vector inversion that kills most AI projects in legal review. Your illustration of vector encryption employed in real world scenarios could help define the next generation of secure AI systems.

Build with embeddings that stay encrypted in use. Create interesting AI solutions that could actually be deployed in production scenarios without the data security risks! Win prizes.

We’ve built encryption-in-use for vector search — delivering sub-millisecond encrypted queries at billion-vector scale. So far, it’s only been tested internally. Now, we need your expertise to prove its utility and push it further.

Whether you’re:

Building RAG systems for healthcare, fintech, or enterprise — help us uncover integration edge cases.
Doing real-time fraud detection — stress-test our latency claims.
Integrating with LangChain or LlamaIndex — find the rough edges we missed.
Deploying on Kubernetes or serverless — challenge our assumptions.

Task

Your challenge is to employ CyborgDB's encrypted vector database using the given themes and provide actionable feedback that helps us refine the product.

Put the database through Deploy the database for your realistic workflows, illustrate its utility, illustrate its utility, and uncover edge cases that reveal where improvements are needed.
Goal: Deliver practical, actionable insights that directly shape the future of secure vector search.

Your feedback is as valuable as your code.