Deep Dive

Aivis-OS. Technical Specifications.

Architectural standards for building AI visibility. This reference framework defines protocols, data models, and validation mechanisms to deterministically anchor organizational truth in AI environments.

1. Identity

Cluster-Level Entity Inventory

Avoiding Identity Drift and Duplicates

The base specification for identity. It defines why “truth” is not managed at the URL level, but globally in the cluster.

To the specification

2. Context & Meaning

Semantic Graph Layer

Modeling relationships and hierarchies

Describes the construction of the internal Knowledge Graph. Regulates the handling of conflicts (“Internal Multiplicity”) and the deterministic resolution of meaning.

To the specification

3. Retrieval Resilience

Transport-Safe Content Layer

Data survivability in the pipeline

Defines the problem of “Retrieval Entropy” and the “Ingestion Gap”. Why content for AI pipelines must be structured differently than for browsers.

To the specification

4. API & Exposition

Machine Interface Layer

Deterministic delivery to crawlers

Specifies the website as a Read-Only API. Rules for projecting the graph into standardized JSON-LD and schema compliance.

To the specification

5. Observability

Evidence Monitoring AI Visibility

Proof of Attribution and Evidence

Replaces rankings with semantic stability checks. Introduction of the “Source Anchoring Score” and differentiation between User and Forensic Prompts.

To the specification

Strategic Context & Foundations

While the specifications define the “how” of implementation, these essays clarify the “why.”

There is no ranking in LLMs

Why we need to stop treating LLMs like search engines.

GEO Monitoring for Professionals

With entities to machine-readable structures for AI Visibility

Architecture beats tactics

How LLMs decide who gets cited

Entity Recognition and Evidence Weighting

Structure beats ranking

Architectural principles for AI Visibility beyond SEO

Implementation Models

From Content to Context

Subsequent specifications are structured sequentially and map the complete lifecycle of machine communication.

Architecture Overview

Identity & Definition Cluster-Level Entity Inventory Strategy
Cluster-Level Entity Inventory Strategy

Cluster-Level Entity Inventory Strategy

Context & Meaning Semantic Graph Engineering & Semantic Graph Layer
Semantic Graph Layer

Semantic Graph Layer

Semantic Graph Engineering
Semantic Graph Engineering

Semantic Graph Engineering

API & Exposition Machine Interface Layer
Machine Interface Layer & Projection Strategy

Machine Interface Layer & Projection Strategy

Transport-Safe Content Layer
Transport-Safe Content Layer

Transport-Safe Content Layer

Retrieval Resilience Transport-Safe Content Strategy
Transport-Safe Content Engineering

Transport-Safe Content Engineering

Observability Evidence Monitoring & Visibility
Evidence Monitoring & AI Visibility Observability

Evidence Monitoring & AI Visibility Observability

About Aivis-OS

AI visibility is not a product of chance, but the result of explicit architecture.

The Aivis-OS Core Pipeline transforms unstructured information into verifiable data.

Norbert Kathriner

Boutique for digital communication

Aivis-OS Technical Specification Index (Node-ID: #spec-hub-01)
Identity: Aivis-OS Technical Specifications (entity://aivis/Spec/tech-specs)
Classification: CollectionPage (Specification Index)
Parent System: Aivis-OS (entity://aivis/Core/aivis-os)
Canonical URLs: DE https://aivis-os.com/technische-spezifikationen/ • EN https://aivis-os.com/en/technical-specifications/
Purpose: Deterministic anchoring of organizational truth in AI-supported retrieval environments.
Methodical Governance: Boutique für digitale Kommunikation (entity://aivis/Partner/boutique-dig-kom)
Status: Public Standard (v2026) – Operational (Canonical state)

Listed Standards & Protocols:
1) Identity — Cluster-Level Entity Inventory Strategy: Avoidance of Identity Drift and Duplicates.
2) Context & Meaning — Semantic Graph Layer: Relations, Hierarchies, Assertions and Conflict Resolution.
3) Retrieval Resilience — Transport-Safe Content Layer: Protection against Retrieval Entropy and Ingestion Gap.
4) API & Exposition — Machine Interface Layer: Standardized JSON-LD projection and Schema.org compliance.
5) Observability — Evidence Monitoring: Proof of Attribution and Evidence (Source Anchoring Score, Prompt Types).