Cross-Engine AI Visibility Audit
Test buyer prompts across AI engines and turn recommendation gaps into evidence
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 6
- Confidence
- 99%
This audit begins with realistic, detailed questions that buyers might ask while researching a product category. The team generates a representative prompt set, submits every prompt to several AI engines, and records which vendors appear, their order, the evaluation criteria, and the reasoning attached to each recommendation. Results are then compared across engines because identical questions can produce different consideration sets and positioning narratives. The output is not merely a ranking table: it highlights where the brand is absent, treated as an add-on, associated with the wrong use case, or recommended only conditionally. Compiling the findings into a concise webpage, document, or PDF creates concrete evidence that marketers can use to prioritize work and secure executive support.
Origin
Extracted from Marketing Against The Grain, where the host demonstrates an audit using buyer questions across ChatGPT, Claude, Perplexity, and Google Gemini.
Core principles
- 01Audit realistic buying questions rather than generic brand queries
- 02Compare multiple AI engines because their recommendations and reasoning differ
- 03Evaluate both whether the brand appears and how it is positioned
- 04Package findings so decision-makers can see the commercial gaps
How to run it
- 1
Model the buyer
Describe the business, product, and target customer, then generate likely questions buyers would ask while researching solutions.
Pro tip Ask an AI tool to research the business and propose 10 likely buyer prompts.
Watch out Do not limit the set to branded searches that presuppose awareness of your company.
- 2
Choose consequential prompts
Select detailed prompts covering categories, alternatives, requirements, company size, current tools, and switching constraints.
Pro tip Use full buying situations rather than short keyword-style queries.
Watch out A narrow prompt set can hide weak visibility in important product lines.
- 3
Run every prompt across engines
Submit the same wording to ChatGPT, Claude, Perplexity, and Google Gemini so the outputs are comparable.
Pro tip Preserve the exact prompt and full response for each run.
Watch out Do not infer market-wide visibility from one engine.
- 4
Score the recommendations
Record whether the brand appears, its rank, the competitors included, and the reasons given for each recommendation.
Pro tip Separate simple mentions from strong or primary recommendations.
Watch out A mention can still expose damaging positioning, such as being framed only as an add-on.
- 5
Diagnose the gaps
Compare products, use cases, and engines to find repeated absences, conditional recommendations, and inaccurate narratives.
Pro tip Prioritize prompts with strong purchase intent and strategic importance.
Watch out Do not treat differences between engines as noise; those differences are part of the finding.
- 6
Compile the evidence
Create a webpage, PDF, or presentation showing prompts, outputs, competitive patterns, and the most important gaps.
Pro tip Lead with examples that connect poor visibility to lost consideration or revenue.
Watch out Raw screenshots without a synthesized diagnosis may fail to win executive support.
In the wild
The host tests a buyer query for a $50 million B2B company seeking AI-powered ticket resolution without replacing its whole stack. HubSpot appears across all four engines but is not treated as the leading option; Zendesk and Intercom receive stronger recommendations. The audit exposes that Service Hub is perceived primarily through ecosystem compatibility rather than service-platform depth.
→ The team receives a specific positioning gap to address instead of a vague objective to improve AI visibility.
A company can ask each engine for alternatives to Salesforce when it is too expensive and complex for a mid-market team. Comparing the answers reveals which vendors enter the consideration set and what switching criteria the models emphasize.
→ Marketing learns whether its product is associated with the buyer's actual reasons for switching.
Common mistakes
Testing only one AI engine
Recommendation sets and supporting reasons vary by engine, so a single result cannot represent the whole AI-discovery environment.
Using only short generic queries
Modern buyers provide detailed situations and constraints; keyword-like prompts miss how models respond to realistic buying contexts.
Counting mentions without reading positioning
A brand may appear while still being framed as secondary, conditional, or unsuitable for the core use case.
Is it for you?
Best for
Marketing teams that need evidence of their brand's visibility and positioning across AI-assisted buying journeys.
Not ideal for
Teams seeking meaningful conclusions from a single branded query or one AI engine.
From the transcript
“You should go and come up with these same prompts for your business.”
“And you can do exactly what I did. You can go one to one-to-one and you can search them and you can see how you…”
“So I strongly recommend you get a set of prompts, you take those, you search them through, and you actually compile a report of what…”
From the episode
We Asked 4 AI Tools About Our Brand (The Result Were Alarming)