Get Recommended By The AI Your Buyers Ask.

Generative Engine Optimization is the practice of making your business the source an AI answer engine cites. Instead of competing for a blue link on a results page, you are competing to be the company ChatGPT, Google AI Overviews, Perplexity, Gemini, or Copilot names when someone asks it who to hire. That takes machine-readable structure, a consistent entity footprint across the web, content written in a directly quotable format, and third-party sources the models already trust.

SEO optimizes for ranking — being one of ten links a person chooses from. GEO optimizes for citation — being the single answer a model gives when no list is shown. They share a foundation (crawlable, fast, well-structured pages) but diverge in tactics: GEO cares about entity consistency, schema depth, extractable answer blocks, and off-site corroboration far more than keyword density or link volume. In practice we run them together, because an answer engine will not cite a page a crawler cannot reach.

Yes. Classical search still drives the majority of commercial traffic, and every major answer engine draws from the same indexed web that SEO builds. GEO is not a replacement — it is the layer that protects you as more of the buying journey moves into AI answers. The efficient path is one program: technical and content SEO as the base, GEO as the layer that makes that base quotable. More on our SEO & content approach →

We track a defined set of buyer prompts across ChatGPT, Google AI Overviews, Perplexity, Gemini, and Copilot, and record how often you are mentioned, how you are described, and which source the engine credits. Alongside that we watch referral traffic from AI assistants in GA4, branded search volume, and the leads that arrive already knowing who you are. The report answers one question: when a buyer asks an AI who to hire, how often does it say you?

Technical work — schema, entity cleanup, crawler access, answer formatting — is usually reflected in AI answers within four to eight weeks, because retrieval-based engines like Perplexity and AI Overviews read the live web. Deeper shifts that depend on off-site citations and model training data take a quarter or more. Anyone promising you a mention next week is guessing.

ChatGPT and ChatGPT Search, Google AI Overviews and AI Mode, Perplexity, Google Gemini, Microsoft Copilot, and Claude. They differ in how they retrieve and cite, but they reward the same underlying profile: clean structure, unambiguous entity data, direct answers, and credible third-party corroboration. We optimize for that profile, then tune for the engines your buyers actually use.

Not directly, and anyone who claims otherwise is selling you something. What you can control is the evidence the model draws from: your own pages, your structured data, your business profiles, your reviews, and what credible third-party sources publish about you. Fix the inputs and the output moves. We have corrected wrong service areas, outdated hours, and stale positioning in AI answers by fixing the sources feeding them.

llms.txt is a plain-text file at the root of your site that gives AI systems a clean, structured summary of what you do and which pages matter. It is an emerging convention rather than a guaranteed ranking factor, but it costs almost nothing, it clarifies your entity to any model that reads it, and it forces a useful exercise: stating plainly what your business is. We publish one on our own site and we set one up for clients.

For a local service business, almost never. Blocking GPTBot, PerplexityBot, or Google-Extended removes you from the answers your buyers are reading while your competitors stay in them. Publishers with paid content have a real case for blocking; a business that wants to be recommended does not. We audit your robots.txt for accidental blocks, which we find more often than deliberate ones.

It works especially well for them. Local prompts — “best HVAC company near Huntington,” “top rated dentist on Long Island” — are exactly the questions people now ask an assistant instead of typing into a search bar. The answers lean heavily on Google Business Profile data, reviews, local directories, and consistent name-address-phone information, all of which are fixable and all of which most competitors have neglected. See our Long Island local search work →

A baseline AI visibility audit across your buyer prompts and named competitors, an entity and citation cleanup, schema implementation across your key pages, restructuring of those pages into answer-ready format, an llms.txt and crawler-access configuration, an ongoing content cadence aimed at the questions buyers ask, and a monthly read-out showing prompt-level share of voice and the leads it produced.

Pricing scales with scope — site size, how many prompts and engines we track, the state of your existing schema and profiles, and how much content we produce. We do not sell hours; we scope against the outcome you need. Book a strategy call → and we will give you a candid range for your situation on the first conversation.