A New Platform Just Started Measuring Brand Visibility Inside LLMs. The Share-of-Voice Metric Has a New Battleground.
AIEthos has launched a platform that measures how specific businesses appear inside ChatGPT, Claude and Gemini conversational outputs. The metric turns LLM share-of-voice into a trackable brand KPI.
If your brand is not surfaced when a user asks an LLM the obvious category question, the click is now going to a competitor named in the AI's answer.
AIEthos has launched a platform built to measure brand visibility inside large language models. The product monitors how specific businesses appear in conversational outputs from ChatGPT, Claude, Gemini and other consumer-facing AI tools. The launch turns a previously invisible metric into a trackable one.
The category itself is new. Until this year, brand visibility meant share-of-search, share-of-voice in media, organic ranking or social mentions. LLM visibility was not measured because the surface itself was not a meaningful traffic source. That has changed. ChatGPT alone has crossed several hundred million monthly users. AI Overviews from Google now serve over two billion people a month. Claude and Gemini are the next tier down but still meaningful in volume.
If your brand is not surfaced when a user asks an LLM what the best CRM for small businesses in Australia is, or who makes the best running shoes for flat feet, the click that used to land on your site is now going to a competitor named in the AI's answer.
Monthly users on Google AI Overviews. The LLM surface is no longer experimental. It is mainstream.
Why it matters
For Australian brands, the LLM visibility question is the next iteration of the share-of-search question. Search visibility was the brand health metric of the last decade. LLM visibility is the brand health metric of the next one. The category is being called GEO, AEO or LLMO depending on the publisher. The methodology is still being settled but the principle is the same. Be findable in the AI's answer.
The challenge is that the answer is non-deterministic. Two users asking the same question can get different brand citations. The same model can produce different answers on different days. Measuring share-of-voice in a system that does not repeat itself requires sampling, statistical methodology and a discipline most brand teams have not yet built.
That is what AIEthos is trying to solve. The platform queries the major LLMs at scale, tracks brand mentions, sentiment and context, and reports the visibility as a metric. Whether the methodology holds up to scrutiny is the next year of validation. The fact that someone is measuring it at all is the news.
What to do about it
Test your own brand inside the major LLMs this week. Ask ChatGPT, Claude and Gemini the obvious category questions in your market. Note which competitors get named. Note whether you do.
Map your brand entity coverage. Wikipedia, Wikidata, business directories, schema markup on your site, third-party reviews. LLMs draw from all of these. Gaps in entity coverage produce gaps in LLM visibility.
Build a monthly LLM visibility review. Pick 20 prompt categories that matter to your brand. Sample answers across three or four LLMs. Track over time. The trend matters more than the snapshot.
Re-look at your PR strategy. LLMs are trained on text. The more credible, third-party text about your brand exists on the web, the more likely the LLM cites you. Press mentions, podcast transcripts, comparison articles. All ingestion fuel.
Do not over-invest in chasing a single answer. The model changes. The training data changes. The output changes. Build a portfolio of credible mentions, not a single optimised page.
Brand visibility used to live on the SERP. It now lives in the chat box.