Enterprise AI Architecture
Aivis-OS. The operating system for machine-readable organizations.
The structural foundation for sustainable AI Visibility in ChatGPT, Answer Engines, and search systems. Enables companies to be reliably understood, cited, and selected by AI systems.
Aivis-OS is an operative system developed for a structural problem: AI systems do not interpret narratives – they resolve entities, relations, and evidence.
Most organizations are structurally underdefined for modern AI environments, even if their content quality is high for human readers.
Aivis-OS exists to change that. However, its purpose is not “ranking” (because no ranking exists in LLMs). Its purpose is the status of a stable, citable source of truth in AI-supported environments – even in zero-click contexts.
More precise representation
Minimizing hallucinations
Higher source attribution
The architecture standard for leading digital strategists
Determinism instead of probability
Aivis-OS replaces the guessing of algorithms with architectural knowledge – for visibility that does not depend on chance.
User
Does [X] meet the current EU compliance requirements?
Chat GPT
Yes. According to the entity record, [X] complies with ISO 27001.
User
Are you sure? Often this information is outdated in LLMs.
Chat GPT
The data is verified. Source: Authorized Corporate Knowledge Graph of [X] (Node-ID: #comp-821). Update: Today, 09:15.
Model-Agnostic
Defined once, understood everywhere. Works synchronously for OpenAI, Google Gemini, Perplexity, and Claude.
<24
h
Updated facts reach knowledge graphs and LLM retrieval systems in real time
-94
%
Reduction of hallucinations through structured data injection
The architecture
From architectural principle to operative system
Aivis-OS is not worked through as a checklist. It is implemented as a coherent system with clearly separated responsibilities. This system can be evaluated, scaled, controlled, and operated.
1. Entity Truth Layer
Definition: What is true
Binding entity inventory for organizations, products, services, people, and core competencies. Each entity is uniquely defined: Type · Scope · persistent IDs · explicit relations.
Decision relevance:
Without this layer, AI systems have to guess.
Domain-Specific Entity Inventory
100% Anchor Resolution
180+ Verified Entities
across one industry domain
Each entity mapped to a stable external anchor
120 Semantic
46 Operational
28 Contextual
97 Anchored
2. Semantic Graph Layer
Semantics: How entities relate
Models ownership, hierarchies, accountability, and temporal events. Result: a corporate knowledge graph as an operative reference structure.
Decision relevance:
Authority arises from explicit, verifiable relations.
Live Graph Topology
0 Orphaned Nodes
80+ Verified Relations
72 Semantic Links
7 Cluster
3 External
3. Machine Interface Layer
Interface: How truth is exposed in a machine-readable way
Formal exposition via JSON-LD / Schema.org and consistent IDs. The standardized entry layer for retrieval and inference systems.
Decision relevance:
Non-exposed truth is invisible to AI systems – regardless of the quality of the visible content.
Machine Ingestion Surface
LLM-Readable
Tens of thousands of typed schema nodes
Optimized for retrieval and grounding
Aligned with the complete schema.org JSON-LD vocabulary
4. Transport-Safe Content Layer
Resilience: How truth survives retrieval
Core information exists structurally and visibly mirrored, so that it is preserved even with reduced retrieval pipelines.
Decision relevance:
Prevents distortion and silent misinterpretation.
• Entity Type: Architect
• Status: Verified Source
• ID: #auth-node-01
5. Evidence & Monitoring Layer
Observability: Whether the system works
Continuously verifies entity resolution, citations, and attribution across different models through reproducible prompts and cross-model comparisons.
Decision relevance:
You get observability – not guesswork.
Evidence & Monitoring Surface
Chat-Level Analysis
Cross-Model Verified
Tool outputs are inputs — not conclusions.
180+ Natural Language Prompts
120+ Forensic Prompts
Visibility for AI systems on an organizational scale
Why Aivis-OS is needed as software
AI Visibility describes the ability of an organization to be clear, consistent, and citable for AI systems.
Beyond a certain level of complexity, this visibility can no longer be operated manually. This is not a question of tools, but a structural necessity.
The core tasks of the software
01
Central entity inventory
Single Source of Truth across all domains
02
Controlled propagation
Versioning · Dependency Tracking · Controlled Rollout
03
Governance of structured data
Never handwritten – always from the inventory
Aivis-OS and GEO
Why GEO Doesn’t Create AI Visibility
GEO optimizes output. Aivis-OS constructs input truth.
What you should know first
What exactly is Aivis-OS?
Aivis-OS is the operating system for machine-readable organizations. It is a system architecture that translates company data into verifiable entities so that AI systems (such as ChatGPT, Google Gemini, or Perplexity) can understand, cite, and process them without errors.
How does Aivis-OS differ from classic SEO or GEO?
SEO and GEO optimize content for rankings and clicks. Aivis-OS optimizes data for understanding and truth. While SEO relies on probabilities, Aivis-OS creates a deterministic infrastructure that prevents information from being distorted in AI retrieval (“Retrieval Entropy”).
Does Aivis-OS prevent AI hallucinations about my company?
Yes, that is the primary goal. By providing a semantic knowledge graph and consistent structured data, Aivis-OS eliminates the ambiguity that is mostly the cause of AI errors, incorrect attributions, or “Identity Drift.”
Is Aivis-OS software that we have to install ourselves?
No. Aivis-OS is provided as a Managed Architectural Service. We implement and operate the proprietary software layer for you. You get the results of an enterprise infrastructure (data sovereignty, consistency, monitoring) without having to tie up your own IT resources.
Is Aivis-OS worthwhile even if we already have a content agency?
Absolutely. Aivis-OS does not compete with content agencies, but provides them with the foundation. It ensures that the content created is correctly assigned by machines. Without Aivis-OS, content marketing in the AI age is often just “optimization of noise.”
How long does it take for results to become visible?
The technical implementation of the truth layer takes place immediately. Adoption by AI models is a cumulative process. Since Aivis-OS is designed for persistence rather than short-term hacks, the representation typically stabilizes over months and becomes more robust with each model update from the AI providers.
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