Core Architecture

The Aegis-Graph architecture is built on the principle of Defense-in-Depth. It replaces traditional monolithic verification models with a Distributed Evidence Swarm that operates across three specialized logic layers.

🏗️ System Overview

The system follows a non-linear reasoning path, where each layer provides evidence to the next until an audit consensus is reached.

graph LR
    subgraph Layer1 [Ingestion & Normalization]
        A[Input Credential] --> B{Data Extraction}
        B --> B1[Visual Evidence]
        B --> B2[Semantic Metadata]
    end
    
    subgraph Layer2 [GraphRAG Evidence]
        B1 & B2 --> C{Reasoning Swarm}
        C --> D[(Institutional Graph)]
        D --> E[Node Evidence Check]
        D --> F[Timeline Consistency]
    end
    
    subgraph Layer3 [Audit & Report]
        E & F --> G{Logic Auditor}
        G --> H[Audit Verdict]
        H --> I[Audit Certificate]
    end

1. The Data Ingestion Layer

At the entry point, the system performs a Normalization process. Whether the input is a digital PDF or a scan, the system extracts two parallel evidence streams:

Visual Evidence: Initial structural analysis for layout consistency (Pixel-level forensics is a roadmap feature).
Semantic Metadata: Extracted text, dates, and institutional names are passed to the reasoning engine.

2. The GraphRAG Evidence Engine

The GraphRAG (Graph Retrieval-Augmented Generation) prototype is the core reasoning layer. It performs traversals across the Institutional Graph, which integrates:

Institutional Evidence Nodes: Local indices synchronized with registry lookups (ROR/OpenAlex).
Historical Timeline Metrics: Review of founding dates, accreditation periods, and institutional lifecycle.

3. The Audit Protocol

A final audit verdict is issued based on the Multi-Agent Reasoning Swarm (MARS) findings.

Logic Conflict Resolution: If different agents find contradictory evidence, the Logic Auditor performs a deep-dive "Chain-of-Thought" (CoT) reasoning to resolve the state.
Evidence Weighting: Each anomaly or supporting fact contributes to a cumulative risk score. The final verdict reflects the weighted confidence in the credential's authenticity markers.

Production Status: Professional verification requires server-side document parsing, issuer evidence, revocation checks, and a signed audit response.

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