Open-Core Architecture & Public Reference Overview¶
RCT Platform / Delentia OS provides an intent-centric constitutional AI SDK built on a 9-Tier architecture. The ecosystem is organized around a clear Open-Core Architecture Boundary: the Public Open-Source Core (Apache 2.0) and the Private Enterprise Full Engine.
Open-Core Design Philosophy
The Public Core SDK ships complete reference implementations for intent scoring, multi-LLM consensus, memory compression, and 5 production reference microservices. High-value enterprise orchestration and proprietary execution clusters are smart-masked within the Private Enterprise Engine.
Architecture Topology & Boundaries¶
┌────────────────────────────────────────────────────────────────────────────────────────┐
│ 🌐 PUBLIC CORE (Apache 2.0 Open Source SDK) │
│ • FDIA Scorer Engine (core/fdia/fdia.py) │
│ • JITNA Protocol RFC-001 Reference (rct_control_plane/) │
│ • SignedAI Multi-LLM Consensus Layer (signedai/core/) │
│ • Delta Engine Memory State Diffing (core/delta_engine/) │
│ • Regional Language Adapter (core/regional_adapter/) │
│ │
│ 5 Production Reference Microservices (Integration Examples): │
│ 1. [analysearch-intent] — Intent Analysis & Search Service │
│ 2. [crystallizer] — Context Compression & Memory State Service │
│ 3. [gateway-api] — Public API Gateway Interface │
│ 4. [intent-loop] — FDIA Control Loop Engine Service │
│ 5. [vector-search] — Basic Vector Embedding Search Service │
└────────────────────────────────────────────────────────────────────────────────────────┘
│
▼ (Commercial Moat Boundary)
┌────────────────────────────────────────────────────────────────────────────────────────┐
│ 🔒 PRIVATE ENTERPRISE ENGINE (Smart Masked Clusters) │
│ • Cluster 1: [Delta Intent Engine Cluster] ──▶ 15 Microservices / 10 Algorithms │
│ • Cluster 2: [Logic & Safety Guard Cluster] ──▶ 20 Microservices / 15 Algorithms │
│ • Cluster 3: [Optimized Nodal Assembler Cluster] ──▶ 27 Microservices / 16 Algorithms │
│ │
│ Proof of Scale & Complexity Metrics: │
│ - Verified by 4,849 Enterprise Tests | 100% Pass Rate │
│ - 0.3% Hallucination Rate Target │
│ - 0.00% System Crash Rate Guarantee │
│ - 74.2% VRAM Optimization via Dynamic LoRA Swapping (<12ms) │
└────────────────────────────────────────────────────────────────────────────────────────┘
Public Core Components¶
1. FDIA Scorer Engine¶
The mathematical core of intent grounding. Every action is scored before execution.
- F (Future/Fulfillment): Execution confidence output score.
- D (Data): Data quality score (\(0.0 - 1.0\)).
- I (Intent): Intent precision exponent (\(I \ge 1.0\)).
- A (Architect): Human-in-the-loop gate. When \(A=0\), output is constitutionally blocked.
2. JITNA Protocol (RFC-001)¶
Canonical reference for intent packet transport (\(I, D, \Delta, A, R, M\)).
3. SignedAI Consensus¶
Jury-based multi-LLM consensus voting (\(\ge 75\%\) consensus threshold, variance \(\le \pm 0.20\)) with SHA-256 cryptographic attestation.
4. 5 Reference Microservices¶
Shipped in microservices/ with 142 dedicated integration tests:
- analysearch-intent: Intent classification and entity parsing.
- crystallizer: Memory state compaction.
- gateway-api: Standard REST/GraphQL gateway.
- intent-loop: FDIA scoring control loop.
- vector-search: Basic vector search backend.
Proof of Scale & Enterprise Metrics¶
- Enterprise Test Suite: 4,849 property-based and integration test cases across Python 3.10–3.12.
- Hallucination Protection: Target \(<0.3\%\) hallucination rate via SignedAI multi-model consensus.
- System Stability: \(0.00\%\) crash rate across production test suites.
- Memory Compression: \(74.2\%\) memory state compression ratio (Delta Engine internal benchmark).