llms-full.txt

# RushAEO Full Capabilities & Forensic Registry Specification

This document provides comprehensive technical documentation, machine integrations, and legal boundaries for the RushAEO compliance registry engine.

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## Capabilities {#capabilities}

### The 43-Point V-I-N-G Standard Matrix
RushAEO executes an automated, point-in-time forensic gap-analysis focusing heavily on EU AI Act transparency rules (such as Article 50) across a proprietary 43-point standard framework:
*   **Verification Protocols**: Inspects frontend structures to ensure AI elements and user-facing conversational points expose required identifier footprints.
*   **Ingestion Safety Checks**: Audits boundary conditions for user data inputs, mapping how telemetry interactively traverses backend structures.
*   **Notice Delivery Integrity**: Validates the visual and machine-readable presence of required operational disclosures at points of human-machine interaction.
*   **Governance Mapping**: Documents the automated state of the system for internal enterprise auditing and technical remediation strategies.

### Diagnostic Interfaces
*   **Instant Compliance Check (Free Preview Scanner)**: An unauthenticated public entry point embedded directly within the primary hero viewport. It executes an asynchronous point-in-time surface layer inspection to instantly flag visual alignment exposure gaps under active transparency directives, returning immediate results via a serverless edge router (`/free-preview`) while masking deep verification points behind the professional tier.

### Architecture & Privacy Paradigm
*   **Zero Data Retention (ZDR)**: The platform explicitly operates without an underlying account database or persistent storage arrays.
*   **Stateless Execution**: No user session data, analyzed URLs, payload inputs, or forensic telemetry logs are stored, cached, or logged down.
*   **Technical Sovereignty**: All security, validation, and forensic processes run in an isolated memory lifecycle to ensure absolute operational independence.

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## Integration Guide & Webhooks {#integration}

### Autonomous Agent Discovery Loop
Autonomous agents can query public website configurations to analyze compliance positioning. The ingestion worker accepts header flags and payload payloads strictly containing target target URL assets.

### Workflow Automation
1.  **Ingestion Request**: A client system triggers a POST request containing the target target URL payload. Alternately, the public interface dispatches an unauthenticated payload to the `/free-preview` edge network routing layer.
2.  **Forensic Processing**: The serverless runtime builds a clean code block layout, mapping elements against the 43-point criteria (or surface flags for preview targets).
3.  **Secure Transmission**: The engine constructs a professional, single-pass PDF remediation blueprint, passes it as an atomic synchronous stream, and instantly purges all transaction data.

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## JSON Schema Specification {#schema}

Modern RAG engines and machine-to-machine agents can validate data exchanges using this strict structural schema layout for automated ingestion pipelines:

```json
{
  "$schema": "[https://json-schema.org/draft/2020-12/schema](https://json-schema.org/draft/2020-12/schema)",
  "title": "RushAEOForensicPayload",
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "format": "uri",
      "description": "The target public website or AI frontend application footprint to evaluate."
    },
    "timestamp": {
      "type": "string",
      "format": "date-time",
      "description": "Point-in-time timestamp of technical evaluation execution."
    },
    "framework": {
      "type": "string",
      "enum": ["EU_AI_Act_Article_50"],
      "description": "The target regulatory transparency directive standard applied."
    }
  },
  "required": ["url", "timestamp", "framework"]
}