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ARDI™ Research Center

Research Methodologies & Standards

How GOSH AI, the AI Recommendation and Discovery Intelligence company, conducts, documents, and validates research on how AI models recommend brands. Every finding is traceable to specific prompts, models, and dates.

Built on observed AI behavior. Real prompts, real models, real outputs.

Observed prompt
ChatGPTGeminiClaudePerplexityGrokCopilotAI OverviewsMentioned in 3 surfacesRecommended in 1
Foundation

Purpose & approach

Why this research exists

The ARDI™ Research Center, part of GOSH AI’s AI Recommendation and Discovery Intelligence platform, documents how AI systems interpret, retrieve, and present information about brands in real world conditions. This research exists to support applied decision making, not academic theory.

It helps organizations understand how AI models actually behave, reduce visibility risk, close discovery gaps, and make informed investments in their AI authority.

The approach is observational and empirical. We design prompts that reflect real user queries, collect AI responses, and compare outputs against real world facts. The focus is on identifying patterns, not isolated anomalies.

Reproducibility: wherever feasible, tests are described so others can reproduce the prompt and output comparison, accounting for model version differences.
Systems observed

AI models & platforms tested

Research draws on the seven AI surfaces buyers actually use, depending on availability and relevance at the time of testing. Model versions are noted when relevant.

ChatGPT
OpenAI
Gemini
Google
AI Overviews
Google
Claude
Anthropic
Perplexity
AI Answer Engine
Grok
xAI
Copilot
Microsoft
AI systems evolve rapidly. All findings are time bound to the date of observation. Model versions, training data updates, and retrieval changes can alter behavior between testing cycles.
Observation model

Two distinct observation layers

The same prompt can produce two different realities inside a model. ARDI™ observes both, because measuring only one hides half of how AI decides who to recommend. Flip between them.

Sample buyer prompt“Which brand makes the best mattress?”
Live retrievalThe model searches the open web in real time, then answers from what it finds right now.
Sources retrieved
WirecutterReddit r/MattressRetailer reviewsNest Bedding (site)
Answer, with citations
Nest Bedding
Surfaces because live sources line up: fresh reviews, forum threads, and its own pages all point to it.
Trained memoryThe model answers from what it already learned, with no live search at all.
Recalled from training
mattressmemory foambed in a boxCasperbig brands
Answer, from memory
Casper
Surfaces because the model learned the household name. The newer challenger is nowhere in its memory.
Same prompt. Different winner. Nest Bedding leads on the Search Path and is invisible on the Learned Path. Measure only one and you miss half the story.
The Search Path measures

Real time retrieval behavior, the source citations and link outs a model surfaces, and what it finds when it actually looks.

The Learned Path measures

Embedded training knowledge, the brand associations and authority signals a model holds, and what it believes without looking.

Why both matter: a brand can appear through one path and be absent from the other. Each produces different exposure, different competitor dynamics, and different signals. A complete picture of AI recommendation behavior requires observing both mechanisms independently.
Rigor

Interpretation standards & scope

How we interpret findings
Repeated or consistent behaviors are prioritized over single outputs
Discrepancies between AI answers and real world facts are noted explicitly
Ambiguity in AI responses is treated as a finding, not an error
Absence of visibility is as meaningful as presence
Conclusions are based on observable evidence, not inferred intent
Known limitations
AI systems may produce different responses at different times
Outputs may vary by user context, location, or session history
Not all systems disclose sourcing or citation logic
Observations reflect behavior at a specific point in time
Findings are directional and descriptive, not deterministic
Editorial standards

Independence, updates & revisions

Research independence

The ARDI™ Research Center operates independently within GOSH AI, documenting real AI behavior to support practice oriented decisions.

Research is not commissioned by third parties

Not optimized for rankings or promotional outcomes

Not written to promote specific tools or platforms

The intent is to document how AI systems behave so organizations can decide with clarity

Living documentation

AI behavior changes over time, so research here is treated as living documentation. It updates, annotates, and supersedes itself while preserving the record.

First read
Baseline behavior recorded, dated, and annotated with model context.
Model shift
An update changes retrieval. New findings logged, the prior version preserved.
Current
The latest reading supersedes the last. History stays intact and timestamped.
Application

How to use this research

These insights can inform strategy, sharpen operational priorities, or combine with a full ARDI™ engagement for execution.

01
Understand AI discovery risk

See where your brand is visible to AI models, and where it is absent from the conversation entirely.

02
Close discovery gaps

Find the gap between your SEO performance and your AI representation. Ranking on Google does not mean AI recommends you.

03
Inform content and entity strategy

Guide content authority, entity structuring, and citation amplification, the core disciplines within ARDI™.

04
Ask better questions

Build a sharper internal understanding of how AI interprets your brand, your category, and your competitors.

A note on application: this research is not a checklist, a guarantee, or a substitute for human judgment. The practical work, including prioritization, implementation, and optimization, is delivered through GOSH AI’s advisory and engagement services.
The standard

Rigor you can trace. Findings you can act on.

Every reading in the ARDI™ Research Center is observed, dated, and reproducible, the evidence base behind our AI Recommendation and Discovery Intelligence platform.