Helix DB, Inc.
Build the knowledge infrastructure for AI agents by providing the fastest and most scalable graph-vector database, built in Rust.
At a Glance
- AI Startups
- Enterprise AI Teams
- Developers building RAG applications
- Companies deploying autonomous agents
AI Tools by Helix DB, Inc.
(1)Helix-DB
Graph Vector Database in Rust
Discussions
No discussions yet
Be the first to start a discussion about Helix DB, Inc.
Latest News
Products & Services
A native graph-vector database built from scratch in Rust, combining semantic search with relationship traversal.
An extremely efficient graph structure version optimized for high throughput, low latency, and local prototyping.
A fully managed, hosted version of the HelixDB database.
Market Position
Positions itself as a faster and more scalable alternative to traditional graph and vector databases by unifying them in a native Rust-based engine, specifically optimized for agentic AI workloads.
Leadership
Founders
George Curtis
CEO & Co-founder. Previously built a home automation system and virtual assistant. Based in London/San Francisco.
Xavier Cochran
CTO & Co-founder. Known as 'the Rust guy' with low-level systems programming expertise. Previously CEO at Odyssey Space.
Executive Team
George Curtis
CEO & Co-founder
Leader in AI infrastructure and agentic systems.
Xavier Cochran
CTO & Co-founder
Expert in Rust and low-level systems engineering.
Board of Directors
Founding Story
HelixDB was created to address the complexity of AI retrieval infrastructure. The founders observed that building retrieval systems for AI applications required juggling separate vector databases, graph databases, and custom syncing software. They set out to build a unified graph-vector database that combines semantic vector types with relationship-oriented graph types, mirroring human cognition.
Business Model
Revenue Model
Open source core with optional managed SaaS (Helix Cloud) and custom Enterprise licensing/support.
Pricing Tiers
Self-hosted, optimized for prototyping.
Managed hosting, scaling, and backups.
Dedicated support, advanced security, and enterprise features.
Target Markets
- AI Startups
- Enterprise AI Teams
- Developers building RAG applications
- Companies deploying autonomous agents
- Retrieval-Augmented Generation (RAG)
- AI Agent Knowledge Infrastructure
- Knowledge Graphs
- Hybrid Search (Semantic + Relational)
- UnitedHealthcare
- Early adopters in the YC ecosystem