Audit. Build.
Implement.
Three steps. Then ChatGPT stops guessing.
Everything ChatGPT, Perplexity, Google AI Search and Claude need to understand you.
The Expert Stage turns expertise into the answer.
richresults.ai builds the Expert Stage that turns the expertise of an organization, a brand or an expert into the answer: the stage on which knowledge, experience and position become visible, verifiable and citable, built from positioning, competence clusters, expert profiles, expert articles, publications, imagery, storytelling and corroborating external signals.
The result: a visible, verifiable Expert Entity.
How the Expert Stage works →
What ChatGPT, Perplexity and Google AI Search need to understand your entity.
What is entity building and why does it matter for ChatGPT, Perplexity, Gemini and Claude?
An entity is a clearly defined, verifiable identity that AI systems can recognize, connect to facts and reference reliably. It is a structured object inside a knowledge graph and can describe an organization, a person, a service, a project or a client.
When ChatGPT, Perplexity, Gemini and Claude can identify your entity through structured data and verified external signals, those signals can reduce ambiguity and support more reliable attribution of information to your organization. Without clear entity signals, your organization remains harder for AI systems to classify.
How is entity building different from just adding schema?
Schema markup is one technical signal. Entity building is the complete construction of a recognizable, consistent and verifiable identity for AI systems.
A fully built entity connects your organization through structured data, knowledge graph references and verified external sources using signals such as sameAs. This helps AI systems understand who you are, what you do and why your organization is credible. Generic schema makes information readable. Entity building makes it clearly attributable.
What is the Expert Stage in AEO?
The Expert Stage is the stage for expertise that richresults.ai designs and builds, and a component of Entity Building. richresults.ai develops positioning, specialist content, publications, expert profiles, imagery, storytelling and external signals for it and translates them into a machine-readable structure with Entity Architecture, Structured Data, JSON-LD and Schema.org.
This makes expertise unambiguously attributable, retrievable and citable for AI systems.
Is my entity unique or based on a template?
The entity for your organization is unique. It is built from your actual work, people, discipline, clients, offers and external signals.
richresults.ai does not use templates for entity building. The structure is built from the real substance of your organization so AI systems do not classify you generically, but describe you specifically and verifiably. This is one of the distinctions our AI Countercheck asks you to look for in any provider you consider, including us.
What does the AEO process look like?
The AEO process consists of three steps. First: Portfolio Audit. We analyze your current AI visibility and identify signal gaps. Second: AEO Optimization. We build the entity layer with JSON-LD schema, entity descriptions, AEO copy and external signal alignment. Third: Full Implementation. Everything goes live directly on your website.
The result is a defined project with a complete deliverable. No open-ended retainer.
What is delivered at the end of an AEO engagement?
At the end, you receive a complete, implementation-ready package. It contains the signals AI systems need to understand your organization, brand or expertise clearly, attribute information reliably and evaluate it as a potential source.
The package is built specifically for your entity, validated and prepared to go live. richresults.ai documents everything that was implemented so future updates for new services, new team members or new positioning remain straightforward.
How do AEO, GEO and LLMO connect?
AEO, GEO and LLMO describe different layers of the same task. AEO structures answers. GEO earns citations and recommendations. LLMO builds model understanding.
All three layers are built on clear, consistent and verifiable entity signals. That is why entity building is the shared foundation.
Let's scope
your entity.
Tell us about your organization. We'll tell you what's possible and what needs to be built.