Draft:VectorX DB
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VectorX DB is a secure, high-performance vector database developed by LaunchX Labs. It is designed to support enterprise-grade AI systems, particularly agentic AI, retrieval-augmented generation (RAG), and intelligent context-aware applications. VectorX DB focuses on enhancing search performance, security, and infrastructure efficiency.
Overview
[edit]VectorX DB was developed in response to common challenges faced in deploying AI agents at scale, including memory limitations, high infrastructure costs, and security vulnerabilities. The platform offers a dedicated memory layer for AI applications, combining fast retrieval speeds, high recall accuracy, and data protection mechanisms suitable for sensitive enterprise data.
Core Technology
[edit]VectorX DB incorporates a proprietary Queryable Encryption system that allows similarity search over encrypted data without requiring decryption. This preserves both privacy and performance, ensuring that vector searches can be performed securely at rest, in transit, and in memory.
It also includes a Hybrid Graph Memory Management system. This combines vector-based semantic search with graph-based entity linkage and memory structures, allowing for cost-efficient scaling and long-term contextual memory in AI agents.
Features
[edit]Key features of VectorX DB include:
- Queryable encryption for privacy-preserving vector search
- Hybrid graph + vector memory architecture
- Fast approximate nearest neighbor (ANN) search
- Multi-modal embedding support (text, images, documents)
- Cloud and on-premises deployment options
- Integration with frameworks such as LlamaIndex and LangChain
- Filtering, client-side encryption, and multi-region support
- Compatibility with Linux, macOS, and Windows environments
Applications
[edit]VectorX DB is used to power AI systems in:
- Enterprise knowledge retrieval
- Autonomous agents for customer service, logistics, and healthcare
- Real-time technical assistance and Q&A over internal documents
- Memory-augmented agents requiring high task completion and low latency
Performance
[edit]Independent benchmarks show VectorX DB achieving sub-10ms query latency and 99%+ recall accuracy under various configurations. The database is optimized to deliver over 1000 queries per second (QPS), while maintaining performance under high workloads.
Deployment
[edit]VectorX DB is available in both serverless cloud and on-premises configurations. The cloud offering supports simple pay-as-you-go pricing and data compliance standards such as SoC 2 and GDPR. The on-prem version allows full control over data residency and system performance, suitable for organizations with strict data sovereignty requirements.
Adoption
[edit]VectorX DB has been adopted by enterprises including large hospital networks and AI-first companies seeking secure and scalable vector memory solutions. The platform supports migration from other vector stores like Qdrant, offering performance and compliance benefits.
See also
[edit]- Vector database
- Retrieval-augmented generation
- Privacy-preserving machine learning
- Artificial intelligence
- Approximate nearest neighbor search
References
[edit]Category:Databases Category:Artificial intelligence Category:Vector search engines Category:Privacy-preserving technologies