Per-Tenant Cryptographic Isolation
Every other graph database uses namespace filtering. xrayGraphDB gives each tenant its own cryptographic boundary through a proprietary isolation architecture. This is cryptographic isolation, not access-control theater.
What This Means in Practice
- A database admin with root access to Tenant A cannot decrypt Tenant B's data — even with a disk image
- Cloning a volume to another machine yields unreadable ciphertext
- Key rotation per tenant without downtime
- Meets FIPS 140-2, SOC 2 Type II, and HIPAA encryption requirements
Multi-Layered Defense
Multiple interlocking security layers designed together — integrity verification, per-tenant encryption, cryptographic signatures, anti-cloning protections, and secrets management integration. Not a single layer bolted on after the fact.
Purpose-Built Architecture
Designed from the ground up for multi-tenant, AI-native workloads. The execution engine, planner, storage layer, and wire protocol are all original eMTAi engineering.
Built for Speed
- Vectorized execution engine — designed for high-throughput analytical workloads
- xrayProtocol — native wire format, 24x faster than legacy protocols
- Persistent storage — zero-indirection reads through a proprietary access layer
- GPU acceleration — optional hardware dispatch for analytics workloads
- Plan cache — 425x speedup on repeated queries
- Predictable latency — deterministic memory management, no stop-the-world pauses
Measured Results
- 0.1ms warm query latency
- 0.3ms point lookup on 17M+ edges
- 100K+ nodes/sec bulk ingest
- 1.8B edges on commodity hardware
Cypher + GFQL + Neo4j Compatibility
Write Cypher as you know it. Use GFQL when dataframe-native syntax fits better. Neo4j-specific queries work automatically with zero changes. Dual-language support is a first-class feature of xrayGraphDB.
Full Cypher with Neo4j Syntax Rewrites
xrayGraphDB automatically detects and rewrites Neo4j-specific syntax to standard Cypher, so applications migrating from Neo4j work without code changes.
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CREATE INDEX — Neo4j's
CREATE INDEX FORsyntax auto-detected - SHOW PROCEDURES — returns xrayGraphDB procedures in Neo4j-compatible format
- shortestPath() — native traversal implementation
- Bolt v5 — full protocol compatibility with Neo4j 5.x drivers
// Works identically to Neo4j CREATE INDEX function_name_idx FOR (n:Function) ON (n.name); // Neo4j-compatible procedure listing SHOW PROCEDURES; // Native shortest path MATCH p = shortestPath( (a:Function {name: "main"}) -[:CALLS*..10]-> (b:Function {name: "render"}) ) RETURN p;
// GFQL: Graph Frame Query Language // Dataframe-native graph queries SET GFQL_CONTEXT tenant='acme-corp'; FROM nodes(label='Function') .filter(complexity > 10) .hop(edge_type='CALLS', depth=3) .groupby('module') .agg(count=count(), avg_cx=avg('complexity')) .sort('avg_cx', desc=true) .limit(20);
GFQL as a First-Class Citizen
GFQL is a native query language for data scientists who think in dataframes. Multi-hop traversals, aggregation, and filtering in a composable pipeline syntax.
- Tenant-scoped sessions — automatic isolation per context
- Multi-hop traversals — depth control and edge filtering
- Aggregation — groupby, count, avg, min, max on result sets
- Composable — chain operations in a single expression
Hundreds of Functions · 90+ Native Procedures
The largest native function and procedure library of any graph database. Hundreds of built-in functions covering strings, math, temporal, lists, maps, vectors, and graph traversal, plus 90+ native procedures for analytics, code intelligence, and reachability — all running inside the engine, with optional GPU acceleration.
- Graph ranking & community:
pagerank,louvain,kcore,hits,connected_components - Centrality & reachability:
betweenness_centrality(4 variants),find_path_*,frontier_profile,topk_reachable - Code intelligence:
dead_code,complexity,security,hotspots,coupling,flow_trace,debt,ownership - ML / vectors:
embed(native ONNX),node2vec,semantic_search,cosine_similarity
EMBED() for Vector Operations
Native vector embedding support directly in query expressions. Store, index, and query high-dimensional vectors without external plugins or separate systems.
// Store embedding on a node MATCH (f:Function {name: "parse"}) SET f.embedding = EMBED("function that parses input tokens"); // Find semantically similar functions MATCH (f:Function) WHERE cosine_similarity( f.embedding, EMBED("parsing logic") ) > 0.85 RETURN f.name, f.module;
Designed For
xrayGraphDB is purpose-built for workloads where relationships are the signal — not just the schema.
Relationship Intelligence
Fraud rings, sanctions networks, money laundering chains. Follow relationships through billions of edges in seconds, not hours.
Graph AI & Memory
AI knowledge graphs, agent memory, RAG with graph context. Native vector embeddings via EMBED() alongside graph traversal.
Geospatial Intelligence
Infrastructure telemetry, supply chain dependencies, logistics networks. Graph + geo + vector in one query.
Cyber Threat Detection
Lateral movement tracing, attack path analysis, IOC correlation. Full path context in milliseconds, not alert fragments.
Healthcare & Life Sciences
Referral networks, drug interaction graphs, patient pathway analysis. HIPAA-ready with per-tenant encryption.
Operational Graph Analytics
Real-time community detection, influence propagation, centrality monitoring. GPU-accelerated analytics on live data.
Architecture at a Glance
Why xrayGraphDB survives workloads that crash other systems.
CUDA kernels
SIMD-accelerated
EMBED(), spatial
Every layer is original eMTAi engineering. The execution engine, storage layout, wire protocol, and encryption architecture are all original eMTAi engineering. GPU acceleration falls back to CPU gracefully when no GPU is available — same API, same results, different speed.