Hi everyone 👋

We’re George and Xav – co-founders of HelixDB

TL;DR

HelixDB is an open-source graph-vector database that brings structure to your un-structured data for RAG and AI applications.

ASK: Can you introduce us to people/companies that are working on Graph/Hybrid RAG that could benefit from better performance or less overhead in their development cycles? Contact me at george@helix-db.com

https://youtu.be/V5viTRj2h68

❌ The Problem

AI is changing at a rapid rate which is fundamentally changing technology. This new tech needs new infrastructure.

Everyone is trying to build AI applications, which often involve dedicated data retrieval for their specific use case. Building these retrieval systems is hard. Previously, we’ve relied solely on vector databases to retrieve semantic matches on tiny snippets of text data. But, this technology is shifting and is relying more heavily on connected data, which comes with better context.

But building these retrieval systems often involve:

1. Vector databases
2. Graph databases
3. Bespoke middleman/syncing software

These setups are complicated, take a lot of time, engineering expertise, and create huge amounts of overhead which makes maintaining them very time consuming and expensive.

✅ Our Solution

HelixDB integrates semantic meaning (through its vector types) with relationships to other data (graph types), a similar model to how we structure information in our brains.

This makes it the best solution for making AI retrieval engines for agents and LLMs.

How we currently do this:

1. Seamlessly integrate vector types into your knowledge graph
2. Type-safe query language that guides developers and agents to write correct queries before they are executed
3. Extreme speeds outperforming industry leaders by 1-3 orders of magnitude

How we’re going to make it better:

1. MCP tools that allow agents to walk the graph themselves, deciding at each step how to traverse the graph based on the schema and available data.
2. Built-in ingestion pipelines for multi-modal data.
3. Built-in embedding-models bringing immediate structure to your knowledge graph.

To get setup, follow the guide in our README:
HelixDB Github

🙏🏻 Our Ask

• Can you introduce us to people/companies that are working on Graph/Hybrid RAG that could benefit from better performance or less overhead in their development cycles? Get them to book a call or email george@helix-db.com
• If you have a Github account, then give us a star 🌟

Your help and support makes a huge difference for this project. Thank you!