AI search optimization for answers, citations, and entity visibility.

Why classic SEO is no longer enough

Classic SEO still matters because crawlability, indexable content, internal links, and useful pages remain the base layer. The change is that discovery is no longer only a ranked list of blue links. Google explains AI Overviews as a search feature that can synthesize an answer when its systems decide generative AI is helpful, and Similarweb 2025 generative AI research shows that people now move through search and AI platforms as overlapping discovery paths rather than one clean channel. Google AI OverviewsSource: Google's official AI Overviews explanation.search.google Similarweb generative AI researchSource: Similarweb analysis of generative AI discovery, referrals, and multi-channel search behavior.similarweb.com

For a B2B team, that means the page has to do more than target a keyword. It has to state the entity clearly, explain who the offer is for, support claims with sources, expose useful facts in structured sections, and connect related pages so a crawler, a retriever, and a human reviewer can follow the same story. SEOH treats this as answer-readiness, not as a promise that any one AI system will cite the page.

What we measure differently

The first difference is the measurement model. A normal rank tracker can show whether a page moved for a keyword, but it will not show whether an AI answer understands the business, selects the right service category, or cites a source that supports the sales story. SEOH uses an AI Visibility Matrix to separate the answer surface, prompt family, cited source, entity gap, wrong or missing fact, recommended content update, and next implementation step.

The surfaces reviewed first are Google AI Overviews, Perplexity, and ChatGPT Search because they represent three common buyer behaviors: a search result with an AI summary, an answer engine built around cited research, and a conversational search experience connected to the web. The exact mix can change by market, but the principle stays the same: we test what the buyer asks, what the system retrieves, what source it trusts, and whether your owned content gives it enough clear evidence to work with. PerplexitySource: Perplexity's answer and research surface.perplexity.ai ChatGPT SearchSource: OpenAI's official introduction to ChatGPT Search.openai.com

The second difference is entity resolution. The page should make it obvious that SEOH is a senior-led B2B partner for SEO, GEO/AEO, white-label delivery, PPC, ORM, AI video, and reporting. The same entity facts need to appear consistently in page copy, internal links, schema, and public profiles. Schema is not a magic switch, but structured data gives machines a cleaner way to read the service, provider, offers, questions, and page relationship. Schema.org ServiceSource: Schema.org Service vocabulary reference.schema.org

How a typical engagement runs

The engagement starts with a scoping conversation. SEOH looks at the business model, market, existing pages, sales motion, compliance constraints, and whether the work is direct-to-brand or white-label for an agency. That call is intentionally manual because AI-search work can create risk if claims, regulated language, customer proof, or confidential client details are pushed into public content too quickly.

Week one and week two are usually the manual visibility audit. The audit checks priority prompts, brand/entity facts, service descriptions, citation paths, schema coverage, content gaps, internal-link paths, and buyer questions. The output is not a generic spreadsheet of keywords. It is a decision document that says what to fix first, what to leave alone, which pages need better evidence, and which claims need founder or stakeholder review.

Implementation then runs in sprints. A sprint can include page rewrites, FAQ expansion, schema mapping, internal links, supporting resource pages, comparison content, or citation-path cleanup. Reporting is usually monthly, with quarterly matrix reviews for teams that need a longer view. The cadence keeps old SEO metrics visible while adding AI-answer observations, so nobody mistakes a single screenshot for a strategy.

What you receive

A scoped AI-search engagement usually starts with an AI Visibility Matrix, a prompt test set, a citation map, and a prioritized implementation plan. For teams that already have content velocity, SEOH can add schema-mapping deliverables, answer-block outlines, entity reference links, and internal-link recommendations. For agencies, the work can be packaged into client-safe reporting notes that explain the methodology without exposing SEOH behind the scenes.

The deliverables stay practical. You receive a list of pages to update, the reason each page matters, the exact section or schema field that needs attention, and the risk attached to each claim. When a new page is needed, SEOH drafts the structure, adds source-backed citations, and flags where founder input, case-study approval, or proprietary numbers are still required. That keeps the page useful before it becomes overconfident.

Pricing

SEOH uses founder-approved pricing bands so teams can qualify the fit before a call: Starter is $450-$1,000 USD, Growth is $1,000-$3,000 USD, and Enterprise is $3,000-$10,000 USD. The final scope depends on route count, market complexity, review requirements, implementation depth, and whether the engagement is direct or white-label. The JSON-LD on this page mirrors those tiers as Service and Offer data, while the visible copy keeps the same ranges clear for people.

FAQ

The FAQ below covers the questions that usually decide whether this work belongs in a marketing roadmap now or later: how AI search differs from regular SEO, which surfaces are tracked, what cannot be guaranteed, how long implementation takes, how white-label delivery works, and how the three pricing tiers differ. The answers are also exposed through FAQPage structured data so the visible page and machine-readable page stay aligned.