AIEthos Debuts Next-Gen Generative Engine Optimization (GEO) Platform to Measure Brand Visibility in ChatGPT, Claude, and Gemini

Another GEO Platform Launch — Why This One Matters

AIEthos is not the first vendor to build tooling around generative engine visibility, but its public debut marks something we've been watching for months: the formalization of GEO measurement into a standalone product category. Where early AEO work happened inside content teams with ad-hoc prompt testing, platforms like AIEthos (and, at a different scope, our own audit engine at aivisibility.cyberg7.com.sg) now give marketers a repeatable signal rather than a gut feel.

The interesting tension is this: the underlying research that defines GEO as a discipline is still very new. Yet the commercial tooling is moving faster than most marketing departments' ability to interpret the results. That gap — between "we have a score" and "we know what to do with it" — is where most B2B brands in Asia currently sit.

What GEO Actually Measures

Before evaluating any platform, it helps to fix the definition. The term was coined in a peer-reviewed paper accepted at KDD 2024. In that paper, Pranjal Aggarwal and co-authors at arXiv (2311.09735, 2024) write: "we introduce Generative Engine Optimization (GEO), the first novel paradigm to aid content creators in improving their content visibility in generative engine responses." That framing — visibility in responses, not rankings on a results page — is the core distinction from traditional SEO.

GEO is not about being listed at position one. It is about being cited, paraphrased, or named inside the answer that ChatGPT, Claude, Gemini, or Perplexity returns to a user. The Aggarwal et al. paper (arxiv.org/abs/2311.09735) tested nine optimization strategies across 10,000 search queries and found that adding authoritative citations, quotation statistics, and fluency improvements boosted source visibility by up to 40% in AI-generated responses. That's the evidence base platforms like AIEthos and our own tooling are building on.

A GEO platform, therefore, needs to answer at minimum three questions:

Question	What it reveals
Is the brand cited at all?	Baseline presence across target LLMs
In what context is it cited?	Positive, neutral, or absent framing
Which content signals predict citation?	Actionable content fixes

AIEthos's announced feature set maps directly onto this structure: it queries ChatGPT, Claude, and Gemini with user-intent prompts relevant to a brand's category, then tracks whether and how the brand surfaces in the response.

What the Data Shows — and Why the Baseline is Alarming

We ran 17 audits across 14 distinct domains since our launch in May 2026 (extracted 2026-05-26). The average AEO score across those sites was 23 out of 100 — a Grade F. Not a single domain in that dataset scored above 50. These are not small hobby sites: they include B2B SaaS companies, professional services firms, and lead-generation agencies based in Singapore and across Southeast Asia.

The pattern we see consistently: sites that rank well on Google — with strong backlink profiles and on-page SEO — still score poorly on AI citation readiness. The reason is structural. Google's crawlers reward keyword relevance and page authority. LLMs retrieve information based on how clearly a page answers a discrete question, how credible the surrounding signals are (author bios, citations to primary sources, structured data), and whether the content can be reproduced as a confident, accurate summary.

Google's own documentation on AI-powered search features (developers.google.com/search/docs/appearance/ai-features) notes that pages surfaced in AI Overviews are evaluated for helpfulness and trustworthiness, not just keyword alignment. That's a signal to content teams that the optimization target has shifted — even within Google's own products, let alone in ChatGPT and Claude where there is no equivalent crawl to rank against.

The AIEthos PRWeb launch announcement (Google News, 2026) emphasizes multi-LLM tracking — the ability to compare citation share across ChatGPT, Claude, and Gemini simultaneously. This matters because the three engines pull from different training data pipelines and use different retrieval strategies. A brand that appears confidently in Gemini's responses may be invisible in Claude's. Measuring one engine and assuming parity across the others is a common and costly mistake.

How to Improve Your GEO Score — Seven Concrete Steps

The Aggarwal et al. research and our own audit dataset point to a consistent cluster of high-impact interventions. These are not content rewrites for their own sake — each one changes a specific signal that LLMs use when deciding whether to cite a source.

1. Answer one discrete question per page. LLMs retrieve content that maps cleanly to a query intent. A page titled "Our Services" answers nothing a language model can cite. A page titled "How B2B SaaS companies in Singapore calculate customer acquisition cost" answers something specific — and gets cited when a user asks that question.

2. Add an author bio with verifiable credentials. Both Google's AI Overviews and third-party LLMs weigh authorship signals. The bio does not need to be long — name, role, relevant experience, and a link to a LinkedIn or published work is sufficient. No bio means no trust signal.

3. Cite primary sources inside your content. The KDD 2024 paper found that adding citations was among the highest-performing optimization strategies — lifting source visibility by measurable margins. Link to the original research, the government data, or the vendor documentation you're referencing. Do not paraphrase it and drop the source.

4. Add FAQ schema markup. Structured data lets crawlers and LLM retrieval pipelines extract your Q&A content as discrete units. Pages with FAQ schema on cyberg7.com.sg's audit clients consistently score higher on structured-data sub-scores than their unstructured equivalents.

5. Write your definitions explicitly. LLMs favor content that defines its own terms. If your page discusses "AI Overviews," define what that means in the context of your product or service. Definitional clarity signals that the page is a primary source, not a tertiary commentary.

6. Build a topical cluster, not a single page. A brand that publishes five interlinked pieces on GEO measurement — covering the definition, the tools, the metrics, the case studies, and the how-to — is structurally more citable than a brand with one long-form piece. LLMs pattern-match authority by topic concentration.

7. Run a multi-LLM citation check quarterly. Prompt engineering shifts. Training cutoffs roll forward. A brand that was invisible in February may be cited by July if its content was indexed after the last training update — or vice versa. Manual spot-checks on ChatGPT, Claude, Gemini, and Perplexity every quarter are the minimum viable monitoring cadence before investing in a platform like AIEthos or our own audit tooling.

Run Your Own Audit

Our audit engine tests your domain against all four major AI engines and returns a scored report in under five minutes — the same methodology behind the 17 audits that produced that average Grade F score. Run yours at aivisibility.cyberg7.com.sg/audit.