Measure
Gather structured visibility data across relevant AI systems, prompts and contexts.
Consultancy
Take AI Visibility into your own hands.
No black box. No visibility voodoo.
We help cultural organisations understand how they appear in AI systems, measure what is actually happening and develop evidence-based strategies their teams can apply independently.
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Visibility without explanation is not enough.
Knowing whether your organisation appears in an AI response does not, on its own, explain why—or which underlying conditions your team can influence.
AURAS-O uses the six dimensions of the Framework to move from isolated visibility scores toward a structured understanding of the factors that may shape representation, interpretation and recommendation.
From data to understanding.
Understand
Interpret the results through the six AURAS-O dimensions and identify where the strongest opportunities, inconsistencies and risks lie.
Develop
Build priorities, actions and internal routines together with the organisation’s team.
The objective is not to hand over a list of optimisation tricks. It is to build the organisation’s own capability to understand and manage AI Visibility over time.
How we work
AI Visibility Understanding
A structured assessment of how your organisation is represented across relevant AI systems and prompts, interpreted through the six AURAS-O dimensions.
Possible componentsBaseline measurement, repeated measurement where appropriate, cross-system comparison, visibility patterns, source and citation analysis, and dimensional interpretation.
Strategy
We work with your team to translate the findings into priorities, questions and practical actions. The strategy is developed collaboratively rather than delivered as a closed external recommendation.
Possible componentsManagement workshops, interpretation of findings, prioritisation, a strategic roadmap, and ownership and responsibilities.
Workshops & Advisory
Build the internal knowledge and routines required to understand, monitor and develop AI Visibility independently. Ongoing advisory is optional.
Possible componentsLeadership briefings, team workshops, capability building, implementation support and periodic review.
What you should expect
Evidence before assumptions
Recommendations should be based on observable data and documented research, not folklore about how AI systems “work”.
Transparent methods
You should understand how measurements are produced, what they can show and where their limitations lie.
Strategy developed together
Teams participate in interpreting findings and setting priorities. The reasoning behind recommendations remains open and understandable.
Capability, not dependency
The objective is to leave cultural organisations better equipped to manage AI Visibility themselves.
No visibility voodoo.
AURAS-O does not promise guaranteed rankings, permanent placement inside AI answers or secret techniques that make an organisation “win ChatGPT”.
AI Visibility is dynamic, system-dependent and probabilistic.
Our work focuses on understanding the evidence, identifying the conditions cultural organisations can influence and developing responsible strategies around them.
Built for culture.
Museums, theatres, festivals, orchestras, heritage organisations and cultural destinations operate differently from consumer brands.
Collections, programmes, artists, places, public mandates and institutional reputation create different visibility challenges. AURAS-O was developed specifically to examine these conditions.
Start with understanding.
If you want to understand how your organisation currently appears across AI systems and what your team can do with that knowledge, let’s talk.