10-Layer Cognitive Architecture
A comprehensive 10-layer stack engineered for enterprise AI infrastructure — from hardware abstraction to autonomous improvement.
Why 10 Layers?
Inspired by the OSI networking model but designed specifically for AI systems. Each layer has a clear responsibility boundary, enabling independent scaling, testing, and evolution.
Unlike monolithic AI frameworks, the layered approach allows enterprises to adopt specific layers incrementally. Start with L6 for orchestration, add L8 for safety, then expand as needs grow.
The architecture currently spans a 62+ runtime-component footprint across all 10 layers, with scaling designed to preserve governance, observability, and operational clarity.
Explore Each Layer Interactively
Select a layer to inspect the service groups and platform capabilities that make up the 10-layer RCT architecture.
All 10 Layers at a Glance
Hardware Abstraction
GPU/TPU management, resource allocation, and infrastructure orchestration across cloud and edge deployments.
Data Ingestion
Multi-modal data pipeline supporting text, images, audio, video, and structured datasets with real-time streaming.
Knowledge Engine
Semantic indexing, vector search, and knowledge graph construction for intelligent information retrieval.
Memory & Context
RCTDB v2.0 with the 9-dimensional universal memory schema — Identity, Sovereignty, Context, Payload, Value, Social, Delta, Verification, Evolution — for persistent context across sessions and agents.
Reasoning Core
Multi-strategy reasoning including chain-of-thought, tree-of-thought, and hybrid approaches powered by the FDIA Equation.
Multi-LLM Orchestration
HexaCore 7-model roster (3 Western: Claude/Gemini/Grok · 3 Eastern: Kimi/MiniMax/DeepSeek · 1 Regional: Typhoon G38 for Thai) with JITNA dynamic routing across all task types.
Agent Framework
Autonomous agent lifecycle management with JITNA Protocol for inter-agent communication and consensus.
Safety & Verification
Consensus-based verification, traceability, and policy controls designed to hold hallucination risk to 0.3% on benchmarked workloads.
Application Layer
Domain-specific assistants, enterprise workflows, and solution packages built on the shared platform core.
Self-Evolving Orchestrator
Continuous self-improvement through performance monitoring, A/B testing, and adaptive algorithm selection.
How RCT Compares
| Feature | RCT Ecosystem | Others |
|---|---|---|
| Architecture | 10-Layer Cognitive Stack | Monolithic / 2-3 layers |
| Memory | RCTDB v2.0 — 9D Schema | No persistent memory |
| Hallucination | 0.3% (SignedAI) | 12-15% typical |
| Multi-LLM | 8+ providers, dynamic routing | Single provider lock-in |
| Self-Improvement | L10 autonomous evolution | Manual updates only |
| Protocol | JITNA open standard | Proprietary APIs |
Explore More
Test Infrastructure v5.4.5
First ever 0-failure milestone — Mar 21, 2026
Ready to Build on the 10-Layer Stack?
Frequently Asked Questions
What is the RCT Labs 10-Layer AI Architecture?
The RCT Labs 10-Layer Architecture is a constitutional AI stack spanning from hardware abstraction at layer 1 to self-evolving orchestration at layer 10. Each layer serves a distinct role in ensuring verifiable, auditable, and governed AI execution across enterprise deployments.
How does multi-LLM consensus work in the architecture?
Multi-LLM consensus operates at the orchestration layer, where multiple AI models independently evaluate a request and a consensus mechanism — governed by constitutional rules — determines the final response. This eliminates single-model bias and reduces hallucination rates.
What is the role of constitutional AI in RCT's architecture?
Constitutional AI principles are embedded at every layer of the RCT stack. They define what the AI system can and cannot do, how conflicts are resolved, and how auditability is maintained. This ensures AI decisions remain aligned with organizational policies and ethical constraints.
How many runtime components does the architecture include?
The production architecture includes 62+ runtime components distributed across 10 layers, covering memory, routing, verification, orchestration, and self-evolution. Each component is independently deployable and composable.
Can the architecture be deployed on-premise?
Yes. The RCT Labs architecture supports on-premise, hybrid, and cloud deployment models. Enterprise clients can isolate specific layers for data sovereignty compliance, particularly in regulated markets such as Thailand under PDPA.