Welcome to VRX Studio Beta
The enterprise Neuro-Symbolic domain adaptation platform that replaces fragile Vector RAG with deterministic versioned graph reasoning, instantaneous zero-retraining policy mutations, and legally defensible auditability.
Why VRX Outperforms Traditional Vector RAG
Key architectural advantages for enterprise policy, healthcare prior-auth, and trade compliance
Zero-Latency Policy Updates
In-memory versioned graph adaptation ($O(1)$) applies regulatory updates, policy shifts, and emergency exceptions instantly without model retraining or re-embedding indexes.
100% Deterministic & Auditable
Eliminates stochastic cosine ambiguity and embedding collision. Outputs are exact discrete tokens tied to specific entity IDs and revision numbers with full provenance traces.
16x Faster & 95% Fewer Tokens
Serializes only the precise versioned subgraph instead of dumping 4KB+ chunks into context. Delivers ~112ms p50 serving latencies and drastically reduced LLM inference costs.
Live Policy Verification (Acid Test)
Automated dual-rule evaluation proves that when a policy updates ($G_1$), the system immediately applies the new rule while retaining 100% accuracy on unchanged policies ($G_0$) with zero regression.
Model-Correlated JWT Tokens
Mint cryptographic JWT access tokens cryptographically bound to specific domain models, tenants, and RPM rate limits. Instant audit correlation from HTTP header to model checkpoint.
Air-Gapped & Sovereign Ready
Zero internet dependency during training and inference. Pre-cached foundation models run completely offline on your internal cloud or air-gapped on-premise infrastructure.
Recommended Onboarding Journey
Review the 4-part domain specification bundle.
Upload business facts or generate synthetic data.
Run policy optimization and verify live updates with zero regression.
Choose tier, mint JWT tokens, and connect API.
RAG vs VRX Neuro-Symbolic Intelligence Demo
Experience the fundamental difference: While traditional Vector RAG struggles with stale embeddings, contradictory policy versions, and heavy token bloat, the VRX Neuro-Symbolic Intelligence Layer provides instantaneous policy updates (< 5ms), 100% discrete token accuracy, and legally defensible auditability with zero model retraining.
Plain-English Guide: What is an Instant Policy Update ("Live Graph Mutation")?
Zero RetrainingIn real business operations, policies change regularly β e.g. holiday return windows extend to 60 days, tariff duty rates change under international trade treaties, or statutory rules expedite healthcare prior-authorizations.
Requires re-chunking documents, re-indexing vector embeddings, or costly model retraining. Because vector search relies on approximate cosine similarity, it often returns conflicting, obsolete policy versions or hallucinates expired rules.
Decouples natural language understanding from business rules. When you update a policy, the graph updates instantly in-memory (< 5 milliseconds) with ZERO model retraining downtime. The AI immediately enforces the new rule with 100% deterministic accuracy.
Traditional Vector RAG
VRX Neuro-Symbolic (NS) Layer
Executive Telemetry & Architectural Scorecard
Empirical benchmark comparing Traditional Vector RAG against the VRX Neuro-Symbolic Architecture for E-Commerce: Return Policies across 1,000 enterprise test queries.
Enterprise Architectural Comparison Matrix
| Evaluation Metric | Traditional Vector RAG | VRX Neuro-Symbolic Layer | Enterprise ROI Impact |
|---|---|---|---|
| Live Knowledge Graph Mutation (G0 β G1) | Re-embedding & chunking lag | < 5 ms atomic in-memory update | Immediate regulatory & pricing compliance |
| Multi-Revision Policy Precedence | Stochastic (conflicting passages) | 100% Deterministic (ORDER BY rev DESC) | Eliminates version confusion & incorrect claims |
| Exact Numerical / Symbolic Recall | 38.75% Exact Match | 100.0% Exact Match | Zero numerical drift or date hallucinations |
| Average Serving Latency | 1,840 ms | 112 ms | 16.4x throughput increase on cloud servers |
| Prompt Context Consumption | 3,420 tokens / query | 168 tokens / query | 95% reduction in GPU inference cost |
| Audit & Provenance Trace | Black-box cosine similarity | 100% Attributed to Node ID & Rev | Legally defensible audit records |
VRX Training Essentials: Prescribed Formats
ecommerce_policyAuthoritative four-part schema bundle for domain reasoning layers. Inspect samples or download ready-to-use templates.
Defines rule changes that update live in < 5ms with ZERO model retraining downtime.
Ingest Domain Data
Target: ecommerce_policyUpload your domain files or synthesize a new multi-revision benchmark dataset for E-Commerce: Return Policies
Option A: Upload Custom Files
Option B: Generate Synthetic Dataset
Automatically creates multi-revision history, train prompt-target pairs, and sealed test queries.
VRX Training Playground
Interactive sandbox to optimize domain intelligence layers and run live policy update verification (Acid Test) with zero regression
βοΈ Advanced Optimization Settings βΌ
Live VRX Production API Console
Query your trained VRX Neuro-Symbolic domain layer securely via REST API. Customers integrate the hosted endpoint directly into production applications.
{
"status": "success",
"domain": "ecommerce_policy",
"prediction": "60",
"unit": "days",
"latency_ms": 112,
"engine": "VRX Neuro-Symbolic Intelligence Layer (Hosted)",
"provenance": {
"node_id": "PolicyRev_Apex_4K_v3",
"graph_snapshot": "G1",
"active_revision": 3,
"deterministic_rule": "Highest Active Revision"
},
"audit_timestamp": "2026-10-12T17:50:00Z"
}
Developer Integration Snippet
import requests
response = requests.post(
"https://api.vrx.ai/v1/query",
headers={"Authorization": "Bearer vrx_live_sk_..."},
json={"domain": "ecommerce_policy", "query": "What is the return window?"}
)
print(response.json()["prediction"]) # Output: "60"