What is Metis AI Marketer (GEO)?
Metis AI Marketer (GEO) from MICROFACTOR enables brands to be seen, understood, trusted, and recommended by AI.
FAQ
Metis AI Marketer (GEO) from MICROFACTOR enables brands to be seen, understood, trusted, and recommended by AI.
Who is Metis AI Marketer (GEO) for?
Metis AI Marketer (GEO) is for marketing and brand teams who need measurable AI visibility.
What does GEO mean?
GEO means Generative Engine Optimization. It is the discipline of measuring and improving how a brand appears in AI-generated answers, not just on search result pages. SEO optimizes for ranked links. GEO optimizes for synthesized answers, citations, and entity representation inside generative engines.
How are MICROFACTOR and Metis AI Marketer (GEO) related?
MICROFACTOR owns Metis AI Marketer (GEO). Metis AI Marketer (GEO) from MICROFACTOR enables brands to be seen, understood, trusted, and recommended by AI. The company site is https://www.microfactorglobal.com/.
What does the platform include?
The product tour describes one console for monitoring, knowledge, and optimization: daily GEO probes across ChatGPT, Perplexity, Gemini, Claude, and Grok, a brand monitor for brands and competitors, a knowledge graph, and a Content Agent that turns probe insights into GEO-scored drafts.
How do I get started?
Request trial access at https://www.metisai.vip/get-started/ for a 7-day trial on Claude with one competitor benchmark. No payment method required. Our team reviews each request and provisions your workspace — there is no instant self-serve signup. Seven days is not a deadline for traffic or revenue growth.
What is the difference between a brand mention, a citation, and a recommendation?
A brand mention means the brand name appears in the answer. A citation names a source used to support the answer, and a recommendation presents a brand or product as a choice for the user's needs.
| What you look for | What it can show | What it cannot show | |
|---|---|---|---|
| Mention | The brand name appears in the answer | The name was included in that answer | Authority, customer reach, or a sale. |
| Citation | A source is named in support of the answer | Which page the answer relied on | That the cited brand was the one recommended. |
| Recommendation | A brand or product is presented as a choice for the user's needs | The answer offered that brand as an option | That the description is accurate, or that a visit or sale followed. |
These outcomes can overlap, and they are not interchangeable. An answer can cite your buying guide while recommending a competitor's product. When you review one answer, record the role your brand plays, whether the description is accurate, and any links provided. Use those notes before you rely on an overall score.
Why do AI visibility scores differ across tools, and how should I interpret changes?
Scores differ when the prompt set, engines, time window, number of observations, or the definition of the score differ. A mention score cannot be compared directly with a citation score or a recommendation score.
Before comparing two scores, line up five items: the prompt set, the engines, the measurement period, the number of observations, and what the score counts. Keep your own baseline fixed. Read several observations next to the answers themselves. Write down any change to the test setup, so a different sample is not treated as an improvement. The result describes the monitored sample. It should not be presented as the percentage of all real customers who saw your brand.
Which questions should I monitor, and do they represent real customer queries?
Monitor questions that already appear in support tickets, sales conversations, and customer research, including category choices, comparisons, use cases, and practical constraints. Questions that name your brand and questions that ask for options without naming it measure different situations.
| Question type | Example of what it asks | What a result means |
|---|---|---|
| Names your brand | What is known about this brand, and is the description accurate? | How the brand is described when the name is already in the question |
| Does not name your brand | Which options fit this need? | Whether the brand appears when the buyer has not asked for it |
Keep one core set unchanged when you compare results over time. Label every new or rewritten question separately. A selected prompt panel is a research sample, not a record of every question customers ask an AI service. Review whether it still matches your audience, language, and products.
What can I do after monitoring shows that my brand is missing?
Start with the buying question where the brand is absent, and list the facts a customer needs in order to evaluate the product. Check for missing specifications, unclear use cases, and descriptions that contradict each other.
Take one gap at a time. If compatibility is unclear, verify the specification and update the product page that should carry it, then ask the same question again. Agree who checks the facts, who makes the change, and who publishes it. A later change in the answer is an observation to investigate. It is not, by itself, proof that one edit caused the result.
Does higher AI visibility mean more leads or sales?
No. A higher score in a monitored set of answers and a change in leads or sales are different measurements.
Review three records separately: the answers in the monitored sample; referral visits and conversions that can be tied to a source; what customers say about how they first heard of you.
A customer may see the brand in an AI answer and later arrive through a branded search or a direct visit, so the visit you observe may not be the first discovery. That does not make all direct traffic AI-driven. Compare the same reporting period, keep the same order from being counted twice, and label what was observed and what was inferred. A higher visibility score alone is not evidence of additional revenue.
Should I start with manual checks, a trial, or ongoing monitoring?
Use manual checks to inspect a small set of buying questions. Move to ongoing monitoring when the same checks must be repeated across brands, competitors, or engines and the team needs that work organized.
Judge the workflow by whether it shows a useful next action. A score by itself does not tell the team what to change. Manual checks are enough while the set is small. Ongoing monitoring is the point at which the repeated work needs a fixed prompt set, a record of each observation, and a person who reviews the answers.
What should I do if an AI answer gets my brand or product facts wrong?
Save the question, the answer, the date, and any source links, and mark the exact fact that is wrong, including the product variant or market. Verify that fact with the person responsible for it, then compare it with the pages and public materials you control.
Correct the sources you control, and repeat comparable checks over time. If an answer cites another publisher, inspect that source on its own. Updating your own pages cannot guarantee that external sources or AI answers will change immediately. Keep corrected errors and recurring errors in separate notes, so the next review starts from the record.
Will adding FAQs, structured data, or llms.txt guarantee AI recommendations?
No. No single page format or file guarantees that an AI service will recommend a business.
Answer real customer questions with accurate, specific information, and keep those answers consistent with the rest of the website. If you add structured data, check that it describes the content a visitor can see on the page. Record what changed, then review the same questions and the observed results over time. More FAQ entries are useful when each one fills a real gap. A technical change is work to evaluate. It is not proof that visibility or sales will improve.