Onboarding with a binding cap, build-out with an agreed end, then maintenance – the entire path, step by step.
Domain-based AI Source Integrity · Aivis-OS
Understood correctly.
“When AI talks about your company, it shouldn’t have to guess.”
Aivis-OS makes your own domain the most reliable source for who you are, what you do, and what applies. This way, you regain control over the interpretation of your own content.
Become the source. Not just be visible.
AI systems reconstruct your company from everything publicly available: current pages, old PDFs, reports, profiles, and contradictory statements. Aivis-OS clarifies this source situation and translates it into a structure that machines can read unambiguously – so that not just any picture emerges, but the right one.
What’s at stake?
AI acts as if it knows your company. Make sure it’s correct. You need Aivis-OS when the machine…
…positions you differently than would be beneficial for your company.
… uses your knowledge but forwards inquiries to others.
… ignores your company because it couldn’t categorize it correctly.
From compact audit to monitored source.
Fixed prices based on count.
No daily rate: The counted conflict groups determine the tier, plus a fixed amount per URL for the full run – and the quoted sum is a binding cap.
The Aivis-OS Network. Architecture, software, and implementation in clear roles.
Diagnosis
The starting point is a diagnosis.
Before Aivis-OS changes anything, it makes visible how your organization is read by AI systems today. The free compact audit counts the conflict groups and estimates your website’s core entities. From this, the first two tasks emerge:
Clarify
Where do the same numbers, data, and facts appear side by side in multiple versions?
Complete
What’s missing for your most important customer questions: services, roles, responsibilities, details?
Your organization is modeled as a coherent, machine-readable reality.
When AI has gaps, it starts to guess. And gaps arise whenever it is unclear what belongs together. 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?
KI-Antwort (Illustration)
Yes. According to the entity record, [X] complies with ISO 27001.
User
Are you sure? Often this information is outdated in LLMs.
KI-Antwort (Illustration)
The data is verified. Source: Authorized Corporate Knowledge Graph of [X] (Node-ID: #comp-821). Update: Today, 09:15.
Model-Agnostic
Once defined, consistently evaluable. Aivis-OS is designed for interoperability with OpenAI, Google Gemini, Perplexity, and Claude.
Timeliness
Updated facts are maintained in the machine-readable representation, instead of being indirectly reconstructed from scattered content with a significant delay.
Precision
Verifiable reduction of misinterpretations in controlled test sets – through structured, verifiable data.
Operating Model
Aivis-OS operates in two phases: Onboarding eliminates the historically accumulated damage. Governance then builds genuine value – scenario by scenario, with an agreed end.
A
Onboarding · one-time · fixed price with cap
First, eliminate historically accumulated damage.
- Compact Audit · free: Count conflict groups, estimate core entities – within 48 hours, a quote with a binding cap is available.
- Clarify & Decide: Each conflict group receives exactly one decision – prepared by agents, authorized by you. The QA gate confirms: decided, implemented, verified.
- Delivery: Only after passing the QA gate is the machine-readable layer built – the complete pipeline run generates structured data for every URL.
At the end, the domain is contradiction-free within the verified scope and every URL is delivered with structured data – a finished state, not an open project.
B
Governance · monthly, optional
And from now on, build real value.
- Build · one scenario per month: Each scenario fully clarifies one important customer question – up to ten entities, up to ten test questions: measure the baseline in AI systems, add missing facts, document the change.
- Maintain & Monitor: The attestation queries nine AI systems at fixed intervals and checks the answers against your approved facts; the conflict watch reads your website and reports new contradictions before they take hold.
As soon as the agreed build-out plan is completed, the build-out price ends – the more affordable maintenance takes over.
The Architectural Components
From architectural principle to operative system
The machine-readable reference structure is built from three core components: Entity Inventory, Semantic Graph, and Machine Interface. Content Parity, Evidence & Monitoring, and ongoing maintenance are part of operating the architecture – they secure, test, and update the system, but do not form the architectural core.
Entity Inventory
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.
Sample metric from a domain-based work status
100% Fixed Identifiers
Domain-Specific Entity Inventory
194 Verified Entities
across one industry domain
Each entity with a fixed identifier · 97 linked to stable external anchors
120 Semantic
46 Operational
28 Contextual
97 Anchored
Semantic Graph
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.
Sample metric from a domain-based work status
0 Orphaned Nodes
Live Graph Topology
80+ Verified Relations
72 Semantic Links
7 Cluster
3 External
Machine Interface
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
Content Parity & Retrieval Resilience
Resilience: How truth survives retrieval
Core information exists in a structured and visibly traceable manner, ensuring its preservation even with reduced retrieval pipelines. Critical statements must not only reside in code but must align with visible or meaningfully extractable content.
Decision relevance:
Prevents distortion and silent misinterpretation.
• Entity Type: Architect
• Status: Verified Source
• ID: #auth-node-01
Evidence & Monitoring
Observability: Whether the system works
Evidence & Monitoring shows whether AI systems represent the organization more precisely after editorial clarification and after machine-readable exposition. In the customer model, this level is called: attestation (measures AI responses) and conflict watch (reads your website).
Decision relevance:
You get observability – not guesswork.
Sample monitoring interface
Chat-Level Analysis
Cross-Model Verified
Evidence & Monitoring Surface
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 is no longer manually manageable. This is not a question of tools, but a structural necessity.
AI Visibility is not a single measure or a manual maintenance project. As soon as multiple pages, terms, people, services, and sources interact, it becomes an infrastructure issue.
Aivis-OS brings identity, relationships, exposure, retrieval resilience, and evidence together in a system that is controllable, versionable, and maintainable long-term. This is precisely the difference between a one-time optimization and a resilient reference structure for AI systems.
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
What GEO cannot achieve
We optimize the input. GEO reacts to the output.
The difference can be tested with a single question – ask it of every provider: When is the project completed? SEO is never finished. GEO isn’t either – same methods, same business model, no defined end. Aivis-OS develops finished states: Onboarding ends with a verified, contradiction-free domain, the build-out has an agreed end – after that, monitoring instead of endless optimization.
What you should know first
Initial questions that should be clarified before a first conversation about Aivis-OS. The detailed collection of terms and questions can be found on the basics page.
What exactly is Aivis-OS?
Aivis-OS stands for Domain-based AI Source Integrity and is delivered as a Managed Architectural Service. A proprietary software pipeline supports the analysis, clarification, modelling, and monitoring of your digital presence so that AI systems can understand, attribute, and cite your organisation more precisely.
How does Aivis-OS differ from classic SEO or GEO?
SEO and GEO optimize content for rankings, clicks, and answer interfaces. Aivis-OS starts earlier: at the reference structure from which machine understanding, citations, and recommendations emerge. While many optimization approaches focus on individual URLs or outputs, Aivis-OS works domain-based on entities, relations, evidence, and machine-readable exposition.
Does Aivis-OS prevent AI hallucinations about my company?
Aivis-OS cannot absolutely prevent AI hallucinations. However, the service reduces central causes: unclear entities, contradictory statements, missing relations, weak evidence, and unexposed core information.
Is Aivis-OS software that we have to install ourselves?
No. Aivis-OS is provided as a Managed Architectural Service. The proprietary software pipeline is the infrastructure behind the service. You receive the results of an enterprise infrastructure—data sovereignty, consistency, attestations—without having to operate the pipeline yourself.
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 initial impact is already created by editorial clarification: contradictions, gaps, and unclear statements become visible and actionable. Adoption by AI systems, however, is a cumulative process: search-supported answers often react within weeks, the foundational knowledge of the models follows over months. Therefore, every change is tracked as a measurement series – baseline, implementation, renewed verification in the subsequent attestation. Not claimed, but measured.
When is the project completed?
This question distinguishes Aivis-OS from SEO and GEO models that have no defined end. With Aivis-OS, the end is stated in the quote: Onboarding ends when all identified conflict groups are decided, implemented, and verified and every URL is delivered with structured data. The build-out ends when the agreed plan is completed. After that, the contract switches to the more affordable maintenance: monitor, re-measure, intervene only as needed. If your business grows, the build-out returns for counted new scenarios. Your scope never grows silently – neither does your price.
What teams can actually do with it.
Aivis-OS is only valuable if the diagnosis leads to concrete work for real teams. That’s precisely why the process doesn’t end with a score or a gap report. It generates different working bases for the roles that then need to act.
Output for Editorial and Content
Editorial does not receive a general request to improve content, but a to-do list per URL: which version applies, what specifically is changed, added, or removed – derived from the decisions of the clarification package, prioritized and actionable page by page.
Output for SEO
SEO does not receive another on-page checklist, but off-SEO tasks on a clarified foundation: Once the versions are decided, off-SEO handles the external entries – profiles, directories, portals, and other citation surfaces are populated with the same facts that your domain delivers. First source. Then echo.
Output for Development
Development does not receive a theoretical block, but ready-to-use building blocks: The data layer enters the CMS via the Aivis-OS plugin or an interface: currently in operation for WordPress, and for other systems via their write interface or the CDN. The handoff remains lean: where it is integrated, how visible content and the machine-readable layer stay consistent, and which individual CDN or enterprise CMS connections run as separate projects.
What changes in practice.
Aivis-OS does not produce the same kind of impact for every organization. The basic problem is similar, but the starting point is not: smaller and medium-sized companies often first need to become clearly readable for AI systems at all. Complex organizations must prevent their reality from becoming vague or contradictory across many pages, roles, products, and statements.
For smaller and medium-sized organizations
Here, it’s usually not substance that’s missing, but machine-readable clarity. Services and expertise are present, but the brand is often not yet strong enough to be automatically classified correctly. Aivis-OS organizes existing content so that your organization becomes readable as a concrete, reliable option for AI systems in the first place.
For complex organizations
Here, content is rarely missing, but rather controllable order. Products, people, programs, reports, regions, and statements exist in parallel but are not always consistently brought together. Aivis-OS creates a common reference structure for this, so that communication, SEO, AI visibility, and technical implementation no longer work with separate truths. Ultimately, some gain machine-readable presence, while others gain machine-readable manageability.
About us
As machines increasingly talk about organizations, organizations must learn to talk to machines first.
Architecture Core: #Entity Inventory: Domain-specific entity and property model · #Semantic Graph: Internal and external entity relations, stable anchors and evidence links · #Machine Interface: Domain-specific JSON-LD / Schema.org projection
Operational Safeguards: #Content Parity & Retrieval Resilience: Alignment between visible content and machine-readable exposition · #Evidence & Monitoring: Baseline, post-editorial and post-deployment observability · #Operational Maintenance: Synchronisation of content, entities, relations, structured data and monitoring prompts
Interoperability References: Schema.org core vocabulary, JSON-LD serialization, RDF-compatible modelling where applicable, ISO 8601 temporal encoding where applicable.
Supported System Classes: Large Language Models, AI-powered retrieval systems and answer engines including ChatGPT, Gemini, Claude and Perplexity.
Certification: No certification claim is made by referencing technical or interoperability standards.