Generative Engine Optimization is the process of engineering your digital architecture so that Large Language Models (LLMs) and AI search engines cite your brand as the definitive answer. Instead of optimizing for website clicks, we optimize your data's mathematical representation (vectors) to ensure it is retrieved and synthesized by AI models.
Traditional SEO optimizes for "blue links" on a search engine results page—a metric that is becoming obsolete. Generative engines provide direct answers (zero-click searches). We optimize your data to be the source of those AI-generated answers, restructuring your unstructured data for machine reading rather than human browsing.
A Share of Model (SoM) audit is our baseline measurement of your current AI visibility. We analyze exactly what percentage of relevant AI prompts mention your brand across major systems (like ChatGPT, Gemini, and Perplexity). This gives us a complete picture of your entity clarity and baseline vector positioning before we deploy technical interventions.
Retrieval-Augmented Generation (RAG) is the framework modern AI systems use to search for and generate answers. During a RAG audit, we run simulations to see exactly how your enterprise data is retrieved, chunked, and synthesized. We identify formatting bottlenecks and restructure the data to ensure flawless machine readability.
AI models do not think in keywords; they think in vectors. We execute a mathematical restructuring of your data to ensure its cosine similarity to high-intent user prompts outranks your competitors in the latent space. We also map strict entity graphs so the AI recognizes your brand as a distinct, factual authority.
Yes. While we optimize public-facing architecture for global generative engines, we also engineer bespoke, private machine learning models. For enterprise clients, we deploy custom architectures to turn proprietary corporate data into 24/7 intelligent assistants and predictive analytics engines independent of public networks.
We engineer autonomous agents powered by advanced LLMs (like OpenAI or Claude) trained exclusively on your private knowledge base. Depending on your needs, these agents can execute independent data analysis, automate internal workflows, or navigate specialized B2B communities to autonomously execute organic authority seeding.
We measure success through strict mathematical visibility metrics. We track the continuous growth of your Share of Model (SoM) across target queries and monitor our proprietary Citatability Score—a metric that predicts the exact likelihood of your content being retrieved, used, and cited by major LLMs.
The Latent Audit costs A$299 as a one-off service. It includes a baseline Share of Model analysis across ChatGPT, Gemini and Perplexity, a competitor semantic gap analysis and a GEO roadmap.
Timelines vary by industry and starting visibility. In our most recent documented engagement, measurable mention-rate improvements appeared within one month of deployment. Authority-driven gains compound over 3 to 6 months as AI systems re-index optimized content, while parametric visibility — being mentioned by models without live search — follows model retraining cycles and takes longer.
We optimize for visibility in ChatGPT, Google Gemini and AI Overviews, Perplexity, Microsoft Copilot and Claude.
No. Latent Analytics specializes exclusively in Generative Engine Optimization — making brands visible and cited in AI-generated answers, not just ranked in Google's blue links. Traditional SEO and GEO are complementary, but they target different systems with different mechanics.
No. Latent Analytics is an independent generative engine optimization agency
based in Melbourne, Australia (ABN 88 718 123 724). It is not affiliated with LatentView Analytics, a publicly listed data analytics company
headquartered in Chennai, India.
ABN: 88 718 123 724
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