Only what AI understands
gets recommended.
AIVA explains why some brands get recommended in ChatGPT, Perplexity, Google AI Overviews, and other AI systems — and others don’t. AN Digital helps SME and enterprise teams turn that insight into the right architecture, clear priorities, and a solid roadmap.
For companies that want to build AI Visibility strategically, not tactically.
Trusted by companies building AI Visibility strategically with AN Digital.
AI Visibility is not a content problem.
It’s an architecture problem.
Many companies already invest in content, PR, SEO, branding, or individual GEO measures — and still don’t understand why they get mentioned in AI systems, or why competitors get recommended more often.
Here’s the problem: visibility in AI doesn’t come from a single channel. It happens where systems discover, classify, weigh, and cite brands.
AI systems don’t judge brands by classic search logic alone. They interpret, prioritize, and recommend brands within new answer contexts.
Anyone who wants to be visible in LLMs like ChatGPT, Perplexity, or Google AI Overviews needs more than content production and technical optimization. What decides visibility is the architecture behind it.
AIVA is the AI Visibility Architecture.
AIVA is the AI Visibility standard developed by AN Digital. The AIVA Framework is the underlying model that explains why some brands are visible in AI systems — and others are not.
Instead of looking at isolated measures, AIVA makes the structural causes visible: technical accessibility, semantic classification, authority, reputation, and citation likelihood.
AIVA is built on a proprietary framework, an international AI Visibility study, and data-driven analysis that shows how AI systems evaluate brands.
That turns scattered visibility activities into a systematic decision framework.
AIVA makes AI Visibility explainable, measurable, and manageable.
The four architecture layers of the AIVA Framework
Ingestion Infrastructure
Entity Authority
Semantic Context
Citation Influence
Why SME and enterprise teams work with AIVA
AIVA is not just a
framework — it’s a system.
The strategic starting point: the AIVA Architecture Sprint
The AIVA Architecture Sprint is the fastest way to understand AI Visibility in a structured way and prepare the right decisions.
In the Sprint, we analyze your current position, assess the relevant architecture layers, and develop a clear target architecture with prioritized action areas.
The result isn’t a generic list of tactics. It’s a solid basis for deciding your next steps.
Compares your current AI Visibility with relevant competitors and market participants.
Analyzes the technical prerequisites for crawlability, processability, and structural accessibility.
Maps the topics, questions, and contexts where your brand is semantically relevant today.
Defines the target architecture for AI Visibility — as the basis for prioritization and execution.
Condenses the most important action areas for management-level decisions.
A prioritized roadmap for your next steps toward AI Visibility.
That turns AI Visibility from a vague innovation topic into a concrete strategic work plan.
From the Sprint to the AIVA Transformation
The AIVA Architecture Sprint builds the strategic foundation. It shows where the decisive levers are, which structural gaps limit AI Visibility, and which priorities follow from that.
In the AIVA Transformation, we translate that foundation into concrete blueprints, governance structures, and prioritized action areas. Analysis and decisions become a solid system for operational execution.
AN Digital supports companies as a strategic architecture partner for AI Visibility.
How companies approach AI Visibility
strategically with AN Digital
AIVA is not just a theoretical model. We work with companies to understand AI Visibility in a structured way, identify the right levers, and build viable strategies from them.
How De Martin AG makes its B2B expertise discoverable for modern AI search systems
Sector: Industry
Region: DACH
How a Swiss pension fund doubled its visibility in Google AI Overviews
Sector: Financial Services
Region: DACH
How a regional and long-distance transport provider became a fixed reference point in AI answers
Sector: Tourism
Region: DACH
How visible brands really are in AI search today
AI Visibility is evolving fast — yet many companies still lack a solid understanding of how brands actually become visible in ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, and other AI systems.
That’s why AN Digital regularly publishes research, analysis, and perspectives on the future of AI Visibility.
Right now, the focus is on our study “State of AI Search 2026: LLM Visibility for Brands”, which AN Digital produced in collaboration with Peec AI. It shows how LLM visibility is evolving, what patterns are already emerging, and what implications this has for brands.
The study is part of the data foundation on which we keep developing AIVA as the standard for AI Visibility.
For decision-makers who want to understand AI Visibility, not just watch it happen.
Why companies start with AN Digital
AN Digital developed AIVA to make AI Visibility explainable and manageable, not just something to watch.
We help companies understand how AI systems capture, classify, and recommend brands — and which architecture decides whether visibility happens or doesn’t.
The result isn’t a report. It’s a system that helps companies understand, assess, and develop their AI Visibility on solid ground.
The starting point is the AIVA Architecture Sprint: a solid strategic foundation for better decisions, clear priorities, and the next steps toward AI Visibility.
