HyperGraph is a scalable database and compute platform designed to handle large changes in data and compute scale. It provides a robust infrastructure for managing and processing vast amounts of data, ensuring performance and reliability as demands grow.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional search engines often struggle with large, complex datasets, delivering imprecise results due to their reliance on keyword matching rather than understanding the underlying meaning of the data. This can lead to missed information, wasted time, and increased costs, especially in mission-critical applications. Existing vector search solutions can be slow and lack the necessary precision for certain use cases.
Solution
Graphium Labs Semantic Search provides a high-performance semantic search platform designed for teams working with large, complex datasets. The platform understands the meaning of data, not just keywords, and delivers exact, context-aware results with 100% recall and precision. Built on a proprietary distributed graph runtime, the search engine scales linearly without bottlenecks and offers low-latency performance, even across billions of records. It is optimized for AI/ML workloads, including native support for RAG pipelines and model observability.
Target Audience
The primary target audience includes teams in legal, compliance, research, AI/ML, healthcare, and finance who require precise and reliable search capabilities for large, complex datasets.
Features
- Graph-based execution runtime for 100x faster retrieval compared to traditional vector databases
- 100% recall and precision, ensuring every relevant result is returned
- Linearly scalable architecture that handles increasing data volume and traffic without performance degradation
- Low latency at scale, with query performance of p99 <100ms and p50 < 1ms across billions of records
- Composable query interface with flexible APIs and SDKs for building complex queries
- Distributed, fault-tolerant deployment for production environments, handling node failure and replication without downtime
- Native support for RAG pipelines, model observability, and retrieval at inference time