Generative Engine Optimization

Traditional SEO is
blind to how
LLMs think.

ChatGPT doesn't crawl. Claude doesn't count backlinks. Gemini doesn't read your meta description. They retrieve from a probabilistic model of human knowledge — and if your brand isn't in that model, you don't exist.

Get Your GEO Audit → See Our Method
The Core Problem

Why AI ignores
most brands.

Problem 01
Vector Space Absence
Your brand's concepts are not sufficiently represented in the high-dimensional embedding spaces that LLMs use to retrieve information. Low cosine similarity to relevant queries = zero citations.
Problem 02
Hallucination Risk
When LLMs have insufficient signal about your brand, they interpolate — generating plausible but incorrect information. This is measurable and fixable.
Problem 03
Competitor Citation Lock
When AI models have strong entity associations for your competitors, they default to citing them — even when your content is more accurate. Reclaiming citation share requires deliberate engineering.
Our Methodology

The Synthetic Footprint Strategy

We engineer three layers of LLM signal simultaneously. Each layer targets a different component of how transformer models retrieve, weight, and cite information.

Layer 01 — Retrieval
Vector Database Inclusion
We structure your content for high-dimensional retrieval. This means JSON-LD entities, semantic HTML, consistent entity naming, and cross-domain co-occurrence patterns that increase your vectors' proximity to target query embeddings.
Target: cosine_similarity(brand_vector, query_vector) > 0.87
Layer 02 — Authority
Citation Engineering
We build the cross-domain entity signals that force LLMs to treat your URL as the primary source of truth. This includes structured data co-citation patterns, authority signals from high-PageRank domains, and topical authority depth mapping.
Target: citation_rank = 1 for ≥ 85% of target queries
Layer 03 — Perception
Sentiment Vector Alignment
LLMs don't just cite you — they describe you. We audit and tune how AI models characterize your brand: the adjectives, associations, and competitive comparisons that appear in AI-generated responses about your domain.
Target: brand_sentiment_score ≥ 0.92 (positive alignment)
LLM Landscape

We optimize for
all four engines.

GEO is not a one-model strategy. Each AI engine has a different training corpus, retrieval architecture, and citation behavior. We map and optimize for all four simultaneously.

GPT-4o
94% share
Claude
91% share
Gemini
88% share
Perplexity
96% share
Start Here

Find out where you
stand in the LLM graph.

Our Neural Scan runs your brand through all four major LLMs, maps your citation gaps, and delivers a technical roadmap to GEO authority.

Request Neural Scan →