TuringDB is a native graph database that uses a columnar in‑memory architecture to deliver sub‑millisecond, multi‑hop queries on billion‑node graphs. It provides zero‑lock concurrency and Git‑style immutable versioning with branching, merging, and time‑travel, ensuring consistent performance, auditability, and reproducible analytics. The platform supports unlimited node and edge properties and is fully compatible with Cypher, enabling rich metadata for AI, digital twins, and real‑time analytics without code changes.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional graph databases struggle with high latency, lock contention between reads and writes, and lack robust version control, making real-time analytics, AI workloads, and auditability difficult at scale.
Solution
TuringDB provides a native graph database built on a columnar in‑memory architecture that delivers sub‑millisecond query latency even on billion‑node graphs. Zero‑lock concurrency separates reads from writes, eliminating performance degradation during data ingestion. Immutable, Git‑style commits enable full snapshot isolation, branching, merging, and time‑travel queries for reproducible analytics and audit trails. The platform supports unlimited node and edge properties, allowing rich metadata storage for digital twins, LLM context, and AI agents. Existing Cypher queries run unchanged, letting users adopt the technology without rewriting code.
Target Audience
Primary customers are data‑intensive enterprises, AI and data‑science teams, and developers building real‑time analytics, simulation, or digital‑twin applications that require high‑performance graph queries.
Features
- Columnar in‑memory storage delivering sub‑millisecond multi‑hop traversals on graphs with billions of edges
- Zero‑lock concurrency that prevents read/write competition and maintains consistent performance under heavy ingestion
- Git‑like versioning with immutable commits, branch/merge operations, and time‑travel queries for auditability and risk‑free experimentation
- Full snapshot isolation guaranteeing that each query sees a consistent graph version
- Unlimited property storage on nodes and edges, supporting large text blocks and numeric data for AI and digital twin use cases
- Native Cypher compatibility, enabling seamless migration of existing graph workloads