VectorBridge.ai provides a self-hosted Generative AI cloud platform for enterprises to deploy and manage LLMs and vector databases. It offers ultra-low latency vector search and zero-trust access controls, enabling secure, real-time AI application development and autonomous agent orchestration.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises require robust, self-hosted infrastructure to deploy and manage large language models (LLMs) and vector databases securely and efficiently. Existing solutions often lack the necessary control over data, performance, and agent behavior, hindering the development of sophisticated, privacy-conscious AI applications.
Solution
VectorBridge.ai offers a self-hosted Generative AI cloud platform designed for enterprises seeking full control over their AI deployments. The platform provides ultra-low latency vector search capabilities, enabling real-time data retrieval for applications and autonomous agents. It features schema-aware storage for organized and queryable vector data, alongside zero-trust access controls to ensure data security and compliance. This integrated approach allows businesses to build and scale custom AI products, orchestrate complex agent workflows, and integrate proprietary business logic through Python functions.
Target Audience
The platform targets enterprises and organizations that need to deploy and manage secure, high-performance AI applications, including those requiring self-hosted LLMs and vector databases.
Features
- Self-hosted GenAI cloud platform for LLM and VectorDB deployment
- Sub-50ms vector search latency for real-time applications
- Schema-aware vector database with schema manager for defined entity types and filters
- File manager with automatic chunking, embedding, and file lineage tracking for content provenance
- Python function registration for secure tool use and agent integration
- Agentic execution graph for orchestrating autonomous workflows
- Zero-trust access control with fine-grained policies, roles, API key scoping, and audit logs
- Support for self-hosted vLLM-based models and cloud APIs
- Streaming HTTP and WebSocket endpoints for low-latency AI communication
- Blockchain integration for verifiable data lineage in AI-generated content
- Isolated environments for each integration, ensuring data separation
- Detailed request logs, tracebacks, and chat history for debugging and monitoring