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Five integrated intelligence pillars composing the Luxoranova platform. Each pillar is an autonomous domain with bounded authority, typed message-passing, and auditable execution.
A 10-tier agent hierarchy — CEO, Portfolio Manager, Risk Manager, Macro Analyst, Technical Analyst, Execution Agent, Compliance Agent, Memory Layer, Knowledge Graph, Human Oversight. Each tier with bounded authority, typed message-passing, and autonomous decision-making within its domain.
Specialized intelligence across eight dimensions: Macro, Company, News, Technical, Social, Behavioral, Portfolio, and Execution. Each stream powered by dedicated agents fusing numerical, textual, and visual market data into actionable signals.
Persistent institutional memory — vector database, knowledge graph, long-term contextual recall, and semantic search over market patterns, research, and historical strategy outcomes. Agents reason over accumulated intelligence, not isolated context windows.
A research track exploring hybrid quantum-classical optimization for derivative pricing (Quantum Amplitude Estimation), portfolio optimization (QAOA/VQE), and strategic coordination (EWL quantum game theory). Cloud access via Amazon Braket, with classical computation as the default runtime. Forward-looking — not yet in production.
Production-grade compliance, governance, and security. Pre-trade risk controls per SEC/CFTC Rule 15c3-5, MiFID II algorithmic testing, EU AI Act transparency logging, SEBI/RBI alignment, SOC 2 / ISO 27001 readiness, and AML/KYC integration. Every agent action logged, attributable, auditable.
Multi-Agent Intelligence decomposes market signals from Financial AI into executable strategies. Knowledge OS provides long-term memory and semantic retrieval. The Quantum Layer optimizes portfolios and prices derivatives. Enterprise OS enforces compliance and audit at every step. Each pillar communicates through typed channels with priority, trace ID, and bounded authority.
Data-driven
Market data, research, and intelligence flow from Financial AI to the agent hierarchy for strategy formulation
Compliance-gated
Strategies decompose into trades routed through compliance checks, then to execution systems
Persistent
All decisions, outcomes, and patterns feed into Knowledge OS for future retrieval and continuous improvement
Every agent request routes through a model router that evaluates provider availability, latency, capability, and cost before selecting the optimal model. The hierarchy does not care which provider answers — it only cares that the answer is correct, fast, and traceable. Fallback chains ensure resilience when primary providers are unavailable.