Audited, versioned starting point of your global identity.
Deployment
Deployment is not installation.
It is the transition from architecture to operation. The transformation of your digital presence into a stable governance model readable by AI systems.
Why deployment needs to be rethought
Aivis-OS is not “rolled out” like a software tool. It is introduced – as a binding governance model for identity, meaning, and resilience.
Visibility in AI systems does not arise from features, but from consistent decisions. Deployment is the moment when these decisions first become real and operationally viable.
Operation instead of setup
Governance instead of gut feeling
1.
Getting started
A controlled pilot deployment
(1 cluster ≈ 10 URLs)
2.
The challenge
Where implementations typically fail
3.
The perspective
How a cluster becomes a scalable operating architecture
Entry format: The pilot
10 URLs: Learn before you scale
A pilot is not a scaled-down rollout. It is a protected space in which architecture is first operated under real conditions – without the pressure of having to deliver global impact immediately.
The pilot is not a test of impact, but a prerequisite for controllability. Anyone who takes this step can scale safely after the pilot phase.
Complete run through all 5 layers on 10 core URLs
Transparency for clear roles in the project
Realistic experience of effort and friction
Test for manageability instead of functionality
Five decision-making spaces
The workshops in the pilot deployment
Each workshop is dedicated to one layer. We do not produce PDF documents, but real operative artifacts.
1. Entity Truth Layer
What exists canonically?
Workshop Detail
Layer 1: Entity Truth
Normalization of identities. We separate the entity from its representation.
Central decision
What exists canonically?
Resulting artifact
Cluster Inventory + Persistent IDs
2. Semantic Graph Layer
What is considered consistent?
Workshop Detail
Layer 2: Semantic Graph
The core of governance. Here we decide on validity and priority.
Central decision
What is considered consistent?
Resulting artifact
Relational Assertions + Resolution Rules
3. Machine Interface Layer
How is truth projected?
Workshop Detail
Layer 3: Machine Interface
The interface to the crawlers. Stability against drift and structural errors.
Central decision
How is truth projected?
Resulting artifact
Validator-stable JSON-LD projections
4. Transport-Safe Content Layer
What needs to be visibly mirrored?
Workshop Detail
Layer 4: Transport-Safe Content
Retrieval resilience. Ensuring that the AI finds what it claims to know.
Central decision
What needs to be visibly mirrored?
Resulting artifact
TSCL blocks + Atomic Information Units
5. Evidence & Monitoring Layer
Which checks prove stability?
Workshop Detail
Layer 5: Evidence & Monitoring
Forensic review. User vs. Forensic prompts for success monitoring.
Central decision
Which checks prove stability?
Resulting artifact
Monitoring protocol + Prompt suites
The “Hard Parts”
Deployment fails in places that sound trivial in theory. We resolve these architecturally before they become operational obstacles.
Validator collisions
When correct identity fails due to formal schema restrictions.
We solve this through the Dual-ID Pattern and strict Core-Alignment.
The Ingestion Gap
When machine projection is more precise than the visible text.
We solve this TRUST issue through Data Parity.
Validity in case of contradiction
When entities are clear, but it is unclear which source dominates.
We solve this through Collapse rules in the Semantic Graph.
Bilingual identity
When bilingualism is misunderstood as a copy.
We solve this through Shared Entity IDs to prevent Identity Drift.
Results
Artifacts instead of promises
What you hold in your hands at the end of the pilot process – reliable foundations for your AI visibility.
Entity Inventory v1
Identity
Semantic Graph Ruleset
Rules for conflict resolution and definition of relation types.
Governance
Machine Projections
Validator-stable templates for JSON-LD environments.
Infrastructure
TSCL Patterns
Patterns for visible truth mirroring in the UI.
Retrieval
Evidence Suite
User & Forensic Prompts for stability control.
Monitoring
Pilot Deployment
Reference scope & decision logic
The following document describes the structural entry into AI Visibility as it is used in regulated organizations. It is not a marketing offer, but a reference framework for scope, responsibility and effort.
Offer
AI Visibility & Machine-Readable Architecture (Aivis-OS)
Reference framework for a pilot deployment in regulated organizations
1. Objective of the pilot project
This pilot project serves as a structured review of the extent to which selected content from an organization can be processed consistently, correctly, and stably under the conditions of modern AI systems (Large Language Models).
The focus is not on short-term visibility or performance effects, but on the architectural connectivity of digital content to AI-based retrieval and response systems.
Within the scope of the pilot, the following will be investigated comprehensibly and reproducibly:
- whether content from the organization is recognized by LLMs as a coherent primary source,
- under which structural and architectural conditions this occurs,
- and where technical, semantic or organizational limits exist.
The emphasis is on consistency · stability · governance capability and not on classic marketing KPIs.
2. Character and classification of the pilot
The pilot is not a scaled-down rollout and not a preliminary stage of automatic scaling.
It is designed as a controlled implementation and learning space in which architecture, content and governance are brought together for the first time under realistic operating conditions.
A clearly defined content cluster of approximately 10 URLs is processed.
This limitation is expressly intended to
- make decision-making logics visible,
- recognize dependencies between content, structure and exposure,
- and to realistically classify expectations regarding effort and viability.
Therefore, the pilot is not a test of effectiveness, but a prerequisite for controllability.
3. Project structure
Three work steps along the Aivis-OS architecture
Work step 1
Architecture & Modeling (Pilot Setup)
Objective:
Establishment of a consistent, machine-readable basic architecture for a defined content cluster.
Scope of services:
- Definition of a thematically coherent content cluster (≈ 10 URLs)
- Establishment of a cluster-wide entity inventory
(separation of identity and URL logic to reduce identity drift) - QID mapping and definition of stable external reference anchors
- Modeling of the Semantic Graph Layers
– explicit relational statements (assertions)
– controlled conflict capability (internal multiplicity)
– Definition of canonical states for external exposure - Derivation and documentation of governance rules
(validity, prioritization, external representation) - Generation of standard-compliant, validator-stable JSON-LD projections
based on content agreement with the frontend - Editorial recommendations for adapting the content according to
Transport-Safe Content Layer (TSCL)
Requirement:
All structured information must be visibly displayed in the frontend. Deviating or invisible data will not be used.
Result of work step 1:
- Consistent entity inventory at cluster level
- Documented graph and governance logic
- Technically valid, standard-compliant JSON-LD structure
- Resilient basis for machine ingestion
Work step 2
Classification, Governance & Expectation Management
Objective:
Establishment of a common, resilient system understanding (technical, professional, organizational).
This work step is an integral part of the pilot.
Treated levels (structured classification):
- Paradigm shift from search to synthesis
- Identity vs. URL logic
- Role of the Semantic Graph Layers
- Internal consistency vs. external determinacy
- Ingestion Gap & Loss of visual logic
- Retrieval Entropy & silent error patterns
- Transport-Safe Content Layer (TSCL)
- Website as Read-Only API
- Evidence Weighting instead of Ranking
- Pilot as signal, not as effect
- Operation instead of campaign
Formats:
- Structured presentations
- Joint review sessions (marketing, development, communication)
- Documented decision bases
Result of work step 2:
- Common governance understanding
- Realistic expectation corridor
- Reduction of later coordination and escalation loops
Work step 3
Analysis, evaluation & scaling perspective
Objective:
Classification of the results under realistic operating conditions.
Scope of services:
- Qualitative analysis of the model reactions
(API-based tests, structured prompts) - Evaluation of semantic stability and source anchoring
- Derivation:
- structural strengths
- systemic limitations
- Definition of the conditions under which scaling is
professionally and organizationally meaningful
Limitation:
The pilot provides architectural evidence, not promises of success or impact.
4. Update & maintenance logic
Prerequisite for structural integrity
AI Visibility is not a static state.
The following applies to all URLs processed in the pilot:
- Quarterly review:
- Contents
- Dates / Events
- Downloads
- structural consistency
- Effort: 2 hours per URL
- Billing only for actual changes
This logic is a prerequisite for maintaining architectural consistency.
5. Effort & Billing
- Architecture & Modeling (Aivis-OS-supported): 5 PD
- Classification, Workshops, Presentations: 10 PD
- Coordination & Coordination: 2 PD
Total effort: 17 person days
Fixed price pilot project: CHF 22,000.-
6. Concluding remark
This pilot project is deliberately not a marketing promise, but a structured decision basis.
It is aimed at organizations that understand AI Visibility as an infrastructure, governance and operational issue.
After completion of the pilot, it can be reliably assessed
- whether scaling makes sense,
- where it needs to be anchored organizationally,
- and what effort can realistically be expected.
Aivis-OS