Graphscale provides a cloud‑native platform that runs graph analytics on multi‑GPU clusters, delivering sub‑millisecond query responses for terabyte‑scale graphs. The service includes an in‑memory cache, dynamic temporal and fuzzy pattern‑matching, and APIs for real‑time feature extraction, supporting fraud detection, risk modeling, and AI pipelines in regulated industries.
Funding
Funding not disclosed
Founders
Product
Problem
Large organizations struggle to run graph analytics on terabyte‑scale datasets because CPU‑bound engines incur high latency, limiting real‑time fraud detection, risk assessment, and feature extraction for AI models. Traditional graph databases also lack native support for dynamic temporal graphs and efficient fuzzy matching, causing analysts to miss critical patterns. Consequently, decision‑makers cannot act on insights quickly enough to prevent financial loss or compliance breaches.
Solution
Graphscale delivers a cloud‑native platform that leverages multi‑GPU clusters to execute graph queries at GPU‑accelerated speeds, reducing latency by up to 50× compared with conventional CPU clusters. The service provides an in‑memory graph cache and optimized pattern‑matching algorithms that handle billions of edges and temporal relationships in real time. By auto‑synchronizing with existing data sources, the platform integrates seamlessly into a company’s data pipeline, enabling continuous ingestion and minimal data drift. Built‑in fuzzy matching and support for property, knowledge, and dynamic temporal graphs empower analysts to run exhaustive what‑if scenarios without performance penalties. The accelerated query engine also exposes high‑throughput APIs for downstream AI workloads, allowing rapid feature engineering and model enrichment. Security and compliance features, such as AML‑focused fraud pattern libraries and reduced false‑positive rates, help regulated industries meet audit requirements while staying ahead of emerging threats.
Target Audience
Primary customers are data‑intensive teams in financial services, security/cyber‑risk, insurance, government, and marketing analytics who require real‑time graph insights for fraud detection, risk modeling, and AI feature engineering. The platform also serves data scientists and analysts building high‑performance graph‑based applications.
Features
- Multi‑GPU cluster architecture delivering up to 50× faster graph traversal versus 32‑thread CPU baselines
- In‑memory graph cache with parallelism for sub‑millisecond query response on terabyte‑scale graphs
- Optimized pattern‑matching engine with native support for dynamic temporal graphs and fuzzy scatter‑gather queries
- Auto‑synchronization connectors for trusted data sources, enabling continuous data ingestion and minimal drift
- Full compatibility with property graphs, knowledge graphs, and heterogeneous edge/vertex schemas
- Scalable API layer for real‑time feature extraction, model training pipelines, and custom analytics dashboards
- Built‑in AML/fraud detection modules that reduce false positives and accelerate compliance reporting
- Role‑based access control and end‑to‑end encryption to meet enterprise security and regulatory standards