Semantic architecture
Entities, intentions, taxonomies, cornerstone pages and internal relationships.
DOSSIER 301
Algorithmic influence
Being found is no longer enough. You must be understood, selected, cited and accurately represented by algorithmic systems.
Operational briefSEOdiscovery
SXOexperience
LLMOAI citation
Operational brief
Build authority machines can read.
Visibility now spans classical search, generative answers, entity graphs, platforms and conversational interfaces. The goal is not to manipulate rankings, but to organise a coherent, useful and verifiable presence.
AR / DOSSIER 301INDEPENDENT ANALYSIS
VISIBILITY ARCHITECTURE
Be understood before being recommended.
Entities, evidence, answers, links and structured data must describe the same reality. Consistency improves discovery, experience, recall and the ability of generative systems to cite a source accurately.
02 / Intervention areas
Intervention areas
Reference content
Precise answers, evidence, authors, sources and quotable formats.
SXO performance
Speed, comprehension, journeys, trust and conversion.
Generative presence
Observe AI answers, representation gaps and improve public signals.
Answer-share measurement
Structured observation of presence in generative answers, search, snippets and conversational environments.
Editorial governance
Roles, validation criteria, update cycles and evidence rules that maintain authority.
03 / FIELD NOTES
Tipping points
Algorithmic influence becomes strategic when an entity’s representation no longer matches reality or objectives.
01ABSENCE
Become visible
Content exists but the entity remains absent from decisive queries, answers and comparisons.
02CONFUSION
Clarify the entity
Namesakes, obsolete information or weak sources blur algorithmic understanding.
03AUTHORITY
Become the reference
An actor has genuine expertise but lacks the structured evidence systems expect.
04CONVERSION
Connect visibility to action
Traffic grows without producing understanding, trust or qualified contact.
04 / OUTPUTS
Authority assets
The strategy produces a coherent set of pages, data and evidence reusable by humans and machines.
Intent map
Questions, audiences, decision stages and expected formats.
Entity graph
People, organisations, services, places, subjects and explicit relationships.
Editorial architecture
Cornerstone pages, clusters, navigation and coverage depth.
Reference corpus
Definitions, answers, studies, evidence, authors and dates.
Structured-data plan
Relevant schemas, property consistency and controls.
AI observatory
Query panel, captured answers, citations, errors and change.
05 / Method
From query to reputation
Keywords remain useful, but sit inside a system of entities, evidence and experience.
- 01Intent mapping
- 02Public entity audit
- 03Content architecture
- 04Evidence and structured data
- 05Search and AI measurement
- 06Editorial iteration
- 07Link every claim to evidence
- 08Update decisive content
06 / FAQ
Frequently asked questions
Clear answers on scope, limits, methods and engagement conditions.
Do you guarantee rankings?
No. Nobody controls search engines. The work improves signals, quality and the probability of being understood and selected.
Does GEO replace SEO?
No. It extends it to generative systems and citation patterns.
Can you use existing content?
Yes: preserve, merge, rewrite or remove according to actual value.
What is LLMO?
Optimising presence in large-language-model systems: understanding, retrieval, citation and representation.
How long before effects appear?
It depends on indexing, existing authority and competition. Early signals often precede durable effects.
Must a large volume of content be produced?
No. The right questions need distinctive, evidenced and maintained answers.
How are AI assistants measured?
Using a stable query panel, dated captures and analysis of cited sources, errors and variation.
08 / CONTACT
Open a private channel
State the context and objective. You will receive a clear initial reading.