SEO services · AI Search

AI SEO Consultant

In business, time is money. Add in the cost of producing content that may take weeks or months to prove itself and you're looking at real opportunity cost. I simulate your content's AI visibility before you publish — so you know whether it'll actually get cited.

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AI Search isn't ranking entire URLs — the systems are looking for short passages to quote in responses.

The Fundamental Difference Between SEO and AI Search

AI Search and SEO share a significant amount of overlap and skills. The fundamental difference is in information retrieval. AI Search isn't ranking entire URLs based on page content — the systems are looking for short content passages to quote in responses. Google has been ranking content passages since the 2021 Passage Ranking update, but AI is a full-scale step forward in that direction.

That's why my content audits include passage similarity assessments and personalization based on your industry vertical. The goal is to create high-scoring content passages and minimize low-scoring sections.

Passage Similarity Heatmap — Passage similarity heatmap — red cells are passages unlikely to be retrieved.

Simulate AI Search Results Before Publishing

The good news: you no longer have to guess and wait months to know whether your content will work. By leveraging NLP libraries and Google APIs, I've developed proprietary tools to measure content semantic relevance and AI retrievability before you publish. These tools provide actionable analysis you can use immediately.

Google AI Search Simulation — AI search simulation — how a query resolves against your passages.

AI Visibility Assessment

Whether you have existing content to update or new content to create, my tools can compare the results ahead of time. If an update will hurt performance, don't publish it. If you're creating a new page, you'll know exactly how it stacks up against the competition.

Methodology

Seven Steps to Simulate AI Citation.

01 · CHUNK

Content Chunking

Break your page into the same passage-level units that retrieval systems work with. Chunking strategy matters — get it wrong and the rest of the analysis is noise.

02 · EMBED

Vector Embeddings

Convert each chunk to a dense vector using the same embedding models the major LLMs use. Currently running against Gemini and OpenAI embeddings.

03 · STORE

Vector Database

Persist the chunks in a vector database so queries can be scored against every passage — yours and your competitors' — in one pass.

04 · RETRIEVE

API Retrieval

Pull embeddings back out through Google and OpenAI APIs that mirror the actual retrieval step happening inside AI Overviews and ChatGPT Search.

05 · SCORE

Weighted Scoring

Combine cosine similarity, semantic overlap, entity overlap and grounding into a composite score per passage. A single number doesn't tell the whole story.

06 · GENERATE

RAG-Based Response

Simulate the AI response using RAG principles against the retrieved passages. You see what an answer engine would likely say — and which passage it would cite.

07 · VISUALIZE

Visualize the Gaps

Heatmaps, similarity scores, retrieval frequency charts. Green passages are covered. Red passages are gaps. That's the whole idea.

→ OUTPUT

A Prioritized Rewrite List

You get the specific passages to add, rewrite, or delete — ranked by expected impact on AI citation probability. No guesswork.

Getting Cited Is the Last Step, Not the First.

Before your content is cited by AI, it needs to be retrievable. I can simulate the retrievability of your content chunks by combining Gemini embeddings with a vector database. You'll see how retrievable your information passages are compared to top competitors — and where you're getting beaten.

Content retrieval frequency — Retrieval frequency by domain — who AI is actually pulling from.

Predict AI Visibility for your content — Predicted citation probability across an AI Overview response.