AI Overviews appear on roughly half of Google searches. Millions of buyers now start in ChatGPT and Perplexity instead of a search bar. Brands without a deliberate AI-search strategy appear in almost none of those answers — and most of your competitors haven't noticed yet.
AI search optimization (also called GEO — generative engine optimization, or AEO — answer engine optimization) is the practice of structuring a business's content, data, and authority signals so that AI systems — Google AI Overviews, ChatGPT, Perplexity, Claude — cite and recommend it by name when generating answers.
MBC Group's AI Search solution makes your business citable: entity and schema architecture, llms.txt implementation, answer-first content engineered for extraction, consistent business data across every surface AI systems read, and authority signals that earn recommendations. Aiden OS monitors your AI visibility continuously and executes the optimizations, with a baseline audit showing exactly where you stand today.
For twenty years, winning search meant ranking — earn position three, collect the click. Generative answers broke that chain: the AI reads several sources, synthesizes one answer, and names the businesses it trusts. The click often never happens. Visibility now means being inside the answer, cited or recommended — and the businesses that get cited aren't necessarily the ones ranking first. They're the ones structured for machines to understand, verify, and quote.
Two facts frame the opportunity: AI Overviews already trigger on roughly half of queries, and only a small fraction of businesses track their AI visibility at all. In local markets especially, the surface is enormous and nearly uncontested.
AI systems don't rank pages — they assemble answers, and they pull from sources that make that job easy and safe. The patterns that demonstrably matter:
The system must resolve who you are: one consistent name, location, service set, and identity across your site, GBP, schema, and the wider web. Ambiguity kills citations — machines don't guess.
Content that answers the question in the first sentences, in plain declarative language, gets lifted. Buried conclusions and marketing throat-clearing don't.
Organization, Service, LocalBusiness, and FAQ schema translate your pages into the machine-readable claims AI systems verify against.
An emerging standard: a file telling AI crawlers what your site is, who it's for, and where its key content lives. Low effort, early-adopter advantage.
Reviews, citations, mentions, and consistent NAP data act as the trust layer — AI recommends businesses whose facts check out across multiple sources.
Active profiles and maintained content signal a business that exists now — the same signals that drive the map pack feed AI's local recommendations.
The strategic insight: these overlap heavily with strong local SEO. That's why we run them as one program, not two retainers.
Where you appear (or don't) across Google AI Overviews, ChatGPT, and Perplexity for your revenue queries. Runs as an Instant Audit (AIO) from live data: llms.txt check, schema validation, entity analysis.
Organization, Service, LocalBusiness, FAQ, and BreadcrumbList structured data implemented and validated sitewide.
Your site described to AI crawlers the way sitemap.xml describes it to Google.
Answer-first pages, question-format sections, and quotable definitions engineered for extraction, produced through the Content Mgmt Suite under your Brand Memory rules.
NAP, reviews, and business facts aligned everywhere AI systems verify. Pairs with GBP Optimization.
Aiden re-checks how engines cite you on scheduled Automations and adjusts; drift gets caught the week it starts, not next quarter.
"Best [service] near me" asked to an AI pulls from GBP data, reviews, and structured local content — exactly the surfaces most local businesses leave unmanaged. And for considered purchases (medical, legal, financial, home services), buyers increasingly ask AI to shortlist before they ever search. Being on that shortlist is the new page one. Our AIO work runs in production today for regulated medical clients — schema, llms.txt, and citation content built under compliance constraints, which is the hardest version of the job.
GEO is optimizing content and data so AI systems cite your business when generating answers — the AI-era counterpart to SEO, focused on being named inside answers rather than ranked in link lists.
It's the next layer of it. The foundations overlap (structure, expertise, consistent data), but GEO adds entity optimization, llms.txt, deeper schema, and answer-engineered content — and measures citations instead of rankings. We run both as one program.
No one honestly can — generative outputs aren't deterministic. What we control are the signals these systems demonstrably weigh: entity clarity, structured data, extractable answers, and corroborated facts. Then we measure the results and iterate.
A plain-text file (like robots.txt or sitemap.xml) that describes your site to AI crawlers: what the business is, who it serves, and where key content lives. It's an emerging standard — implementing it now is a cheap early-mover advantage.
You test the queries that matter and log the answers — which is exactly what our baseline audit does, then re-checks on a schedule. Most businesses have never looked; the baseline is usually eye-opening in one direction or the other.
Arguably most of all. Local AI recommendations pull from GBP, reviews, and structured local data — signals you can directly control, in a competitive field where almost nobody else is trying yet.
Technical signals (schema, llms.txt, entity fixes) are read within weeks. Citation behavior shifts as engines re-crawl and corroborate — typically visible movement in 2–4 months, measured against your baseline.