The Latent Analytics methodology is a four-phase generative engine optimization process: a latent space audit of how AI models currently represent your brand, a custom GEO architecture, deployment of scripts and AI agents, and continuous Share of Model measurement. Each phase produces quantifiable metrics, so you always know how your AI visibility is changing and why.
Our GEO optimization methodology is built on scientific
data analysis, not guesswork. We use vector space analysis
to measure how AI models perceive your brand, then apply
targeted interventions to improve your citatability score
and Share of Model across ChatGPT, Perplexity and Google
AI Overviews. All models are included in our Audit as recommended by Google Search Central guidelines.
Unlike traditional SEO agencies, we treat generative
engines as distinct systems requiring distinct strategies.
Our process is repeatable, measurable and designed to
deliver compounding results over time.
We use rigorous data science to analyze, optimize, and measure your brand’s position in AI’s latent space. See our Services
We build bespoke, private machine learning models tailored to your specific business intelligence needs. This goes beyond GEO optimization methodology.
We design and train proprietary NLP pipelines, custom entity recognition systems and predictive data models that run entirely within your own infrastructure — no third-party dependencies, no data leakage, full control.
This service is for organizations ready to move from optimizing for public AI models to deploying their own proprietary intelligence. Whether you need independent
data analysis, advanced semantic classification, or a private knowledge base that powers internal decision-making, we build the architecture from the ground up.
Every model is built on scientific-grade data engineering: clean inputs, explainable outputs, and measurable performance benchmarks from day one.
Wanna know more? Contact us!
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