Vitruvyan OSArchitecture Overview

A modular cognitive infrastructure for explainable, human-centric AI.

System Layers

Five cognitive layers that transform data into understanding

Technology Stack

Open-source and proprietary technologies powering the epistemic framework

backend

  • Python
  • FastAPI
  • CrewAI
  • LangGraph

databases

  • PostgreSQL
  • Qdrant

frontend

  • Next.js (v0.dev)
  • Tailwind
  • shadcn/ui

infrastructure

  • Dockerized microservices
  • Grafana
  • Prometheus

security

  • Keycloak
  • JWT

auditability

End-to-end traceability and data integrity across reasoning layers.

  • Tron Blockchain Ledger
  • OpenMetadata

Proprietary
Cognitive Technologies

Five breakthrough engines that enable transparent, adaptive intelligence

VEE

Explainability Engine

Multilayer reasoning audit

VARE

Adaptive Risk Engine

Context-aware risk assessment

VSGS

Semantic Grounding System

Entity relationship mapping

VWRE

Weighted Reverse Engineering

Decision factor analysis

VMFL

Memory Feedback Loop

Continuous learning system

Auditability and Provenance

Verifiable cognition where data, models, and reasoning coexist under permanent traceability

Vitruvyan OS introduces a dual-layer audit mechanism:

Blockchain Anchoring

Tron

Each reasoning batch (signals, decisions, validations) is hashed and anchored to the Tron testnet or mainnet. This ensures the integrity of analytical outcomes over time and creates a permanent, verifiable record.

Metadata Lineage

OpenMetadata

Every dataset, agent, and API is tracked through OpenMetadata, linking cognitive processes to their provenance and version history. This makes it possible to reproduce any inference and understand the exact context of a decision.

Together, these layers define Vitruvyan's commitment to Auditability by Design — verifiable cognition where data, models, and reasoning coexist under permanent traceability.

LOGOS Ontology Layer

LOGOS will add structured semantic reasoning, enabling Vitruvyan to understand entity relationships and causal hierarchies. This layer bridges symbolic knowledge with neural embeddings, creating a unified cognitive framework.

Design Principles

1

Explainability First

2

Composability

3

Human-Centric Modularity

4

Cross-Domain Adaptability

5

Transparency by Design