Positive trends in 30 days or your first month is free

Your brand in every ChatGPT and Perplexity answer.
Engineered, not hoped for.

Traditional SEO doesn't work for LLMs. Different retrieval patterns, different ranking signals. We optimize for how AI platforms actually retrieve and cite content.

Read the Research

We have built AI visibility strategies for Babbel, G-Star RAW, Charles Tyrwhitt, Ideal of Sweden, Everdrop, and 20+ other brands

Why traditional SEO fails for LLMs

LLMs do not rank pages. They retrieve passages and cite sources. The optimization model is fundamentally different.

0
LLMs don't use traditional keyword matching
Retrieval-augmented generation
88%
of LLM retrieval queries have zero search volume
Ekamoira fan-out research
3
platforms with different retrieval behaviors
Google AI, ChatGPT, Perplexity
40+
sub-queries generated per user question
Average fan-out depth

Ranking is not citation.
Different systems, different rules.

Google ranks pages by authority. LLMs cite passages by relevance. Optimizing for one does not guarantee the other.

How Google ranks

  • Index pages by keywords
  • Rank by backlinks and authority
  • Position-based results
  • Same results for everyone
  • Optimized with meta tags and keywords

How LLMs cite

  • Retrieve passages by semantic similarity
  • Cite based on answer relevance and structure
  • Citation-based inclusion
  • Personalized and context-dependent
  • Optimized with citability and passage structure

Platform-specific optimization

Each platform retrieves and cites differently. We tailor the approach to each one.

Google AI Mode
Google AI Mode

Google's AI Mode generates its own retrieval queries. We map this fan-out pattern and optimize your content to be selected during retrieval.

ChatGPT
ChatGPT

ChatGPT Search uses Bing's index but applies its own relevance scoring. We optimize for ChatGPT's citation patterns and passage selection.

Perplexity
Perplexity

Perplexity searches the web in real-time and synthesizes answers. We structure your content to be retrieved and cited in Perplexity's answers.

The approach

Three stages. Your content in, cross-platform citations out.

Map

Map LLM Retrieval

We analyze how each LLM decomposes your target queries. Fan-out analysis reveals the 40+ sub-queries generated per question — the actual retrieval surface your content needs to cover.

Score

Score Citation Probability

Each query gets platform-specific CPM scores — your likelihood of being cited by Google AI, ChatGPT, and Perplexity independently. Different platforms, different scoring.

Build

Build Citability

Content restructured for LLM citation: clear passage boundaries, direct answer formatting, structured data, and topic coverage matched to each platform's retrieval patterns.

Each 30-day cycle produces seeds for the next. After 3 cycles, the system compounds.

Investment

Month-to-month. No lock-in. Scale up or down anytime.

Discovery

The playbook — your team executes

$1,500/month
  • 25 queries tracked across Google AI, ChatGPT & Perplexity
  • 0 — roadmap only, your team executes
  • Async support via Slack/email
Most Popular

Growth

We build and optimize

$3,000/month
  • 50 queries tracked across Google AI, ChatGPT & Perplexity
  • Up to 15 content actions/month
  • 2 strategy sessions/month
  • 30-day trend guarantee

Accelerated

Full velocity — we own your AI search presence

$5,000/month
  • 100 queries tracked across Google AI, ChatGPT & Perplexity
  • Up to 30 content actions/month (floor, not ceiling)
  • 4 strategy sessions/month
  • 30-day trend guarantee

Frequently asked questions

Common questions about LLM optimization

Stop hoping LLMs find you.
Start engineering citations.

Citation probability scoring across ChatGPT and Perplexity. 88% of retrieval queries are invisible to keyword tools \u2014 our framework maps them.

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