A prospective customer asks an AI assistant to recommend a local accountant, a software tool or an agency. The answer names a few businesses, perhaps with links or supporting sources. A company that does not appear has no position to inspect in a conventional search ranking report. And a passing mention is not the same as being recommended.
That gap is what R-U-Known is designed to examine. It is a paid, web-based tool for companies and agencies that want to assess how AI-generated answers represent a brand. Rather than tracking only whether a name appears, it separates answers into three states: the brand is absent, present without being recommended, or actively recommended. The distinction is useful because those outcomes suggest different questions to investigate.
It starts with questions customers might ask
R-U-Known uses a selected set of customer questions as the basis for a benchmark. The product describes this as a 50-question assessment, covering topics such as a company’s services, category and buying decisions. The workflow is to choose relevant questions, run a benchmark against them, and then inspect the answers and evidence associated with the results.
That makes the product less like a general-purpose SEO dashboard and more like a structured audit of a particular slice of AI search. A business might want to know whether AI recommends it for the services it actually sells. An agency could use the same approach to look at a client’s presence against the questions prospective customers are likely to ask. The value depends on choosing questions that reflect real buying situations. A benchmark built around irrelevant or overly broad questions can give a tidy but unhelpful picture.
The central problem is not simply whether AI has encountered a company name. It is whether the brand is connected to the right services and included when an answer presents options. R-U-Known is built to make that distinction visible at the level of individual questions, rather than leave it buried in one overall score.
The evidence is part of the report
For each result, the product says users can inspect the original question, the generated answer, the visibility classification, competing brands and available supporting sources. It also groups information about domains and specific pages associated with answers. That can help a marketer see where competitor recommendations appear alongside particular publications, directories or comparison pages, and where the company’s own site is absent from the evidence.
The qualification matters: a page associated with an answer is not proof that it caused the AI to recommend a competitor. R-U-Known’s description explicitly treats source information as evidence to investigate, not a causal explanation. That is a sensible boundary. The tool can point to places worth reviewing, but the user still has to decide whether a source is relevant and what, if anything, to change.
This makes it a diagnostic product, not an automatic fix for weak visibility. It can surface content or source opportunities, but the company or agency must do the work outside the benchmark and determine whether the changes make sense.
Snapshots show movement, not a live pulse
The product’s comparison model is based on repeatable benchmarks. It preserves a question set and the results from a run, then lets users compare that snapshot with a later one. The idea is to make changes to a site or online presence, allow time for those changes to be discovered, and run the questions again. Agencies can also mark work between assessments to give movement some context.
This is useful for tracking direction without reacting to every answer fluctuation. It is also a limitation: R-U-Known is not presented as continuous, daily monitoring. A user needs to run another benchmark to see what changed, and even then, AI answers can move for reasons unrelated to the user’s work. The comparison can show a change in visibility, but it cannot by itself establish why that change happened.
There is another practical unknown for a prospective buyer. The product’s overview does not identify which AI search services it tests or explain how answers are sampled. Those details matter if a business needs coverage of specific systems its customers use. It is worth confirming that the assessment matches the intended monitoring scope before treating its results as representative of AI search as a whole.
A focused audit, not a replacement for SEO work
R-U-Known is aimed at businesses that want to know how AI describes and recommends them, and at SEO agencies that need an evidence-led way to discuss that visibility with clients. Its strongest fit is a team willing to examine question-level results, check the sources and competitors, and make its own changes before measuring again.
It is not a substitute for conventional SEO reporting, nor does it appear to implement fixes or prove that any particular page caused an answer. For teams looking for a live monitor or a tool that makes optimization decisions on their behalf, this is a narrower fit. For companies and agencies that need a repeatable audit of AI recommendations, R-U-Known offers a defined process: ask relevant questions, inspect what the answers contain, and compare the evidence later.
Know where you stand in AI search