GEO
Generative engine optimization gets your pages read and cited by AI answers. When someone asks ChatGPT or Perplexity a question in your category, GEO is why your page — not a competitor's — is the one the engine quotes.
MeetGEO · Consulting
AI is already sending your customers to your competitors.
You are not in the answer.
Right now, AI engines are answering questions about your market and naming their picks. Most websites are invisible on at least two of the six surfaces buyers use — not because the product is worse, but because the site isn't structured the way AI reads. MeetGEO fixes that in thirty days, across generative engine optimization (GEO), answer engine optimization (AEO) and agentic commerce optimization (ACO), on your site, with your team, and leaves you the process to keep it running. Fixed fee. No retainer.
Fifteen minutes of questions, five of honest answers about whether this is worth your money.
AI doesn't reward the best product. It rewards the page it can read.
It is not new. It is not a niche. It is ChatGPT, Perplexity, Google AI Overviews and AI Mode, Gemini and Copilot — and it is already deciding who gets the call. Those engines quote a handful of pages, name a handful of businesses, and send the rest of the market nothing. Most websites fail the first check an AI runs before it will cite them. Nobody inside the company knows, because no analytics report shows it.
The strategies that won Google do not win here. AI does not reward the longest page, the most backlinks or the best-written brand story. It rewards the page it can read, the sentence it can lift whole, the number it can verify and the business it can describe in one line.
And that is the answer engine problem. The agentic commerce problem is a layer harder. Perplexity Shopping, OpenAI Operator, Amazon Rufus, Google's AI-powered shopping and Apple Intelligence are already acting as purchasing agents — browsing, comparing and transacting on a customer's behalf without a human reviewing a results page. When those agents run a query in your category, they are not looking for the best headline. They are reading your product schema, checking your price fields, verifying your availability flag, confirming your shipping details and testing whether your checkout is machine-navigable. If any of those fields are missing, mismatched or locked behind JavaScript, the agent skips you. Not demotes you. Skips you entirely, before a human ever sees your name.
That is a different discipline, with different owners, and it is the one that now decides who gets the inquiry, the booking and the sale.
The engagement
One engagement. Three disciplines. Thirty days.
AEO
Answer engine optimization makes your page the answer the AI gives, not a footnote under someone else's. AEO is the structural work that turns your key pages into the source an engine reaches for first.
ACO
Agentic commerce optimization makes your business the one an AI shopping or booking agent selects when it acts on a customer's behalf. ACO is the machine-readable data layer that puts you in consideration when a human never touches the search.
MeetGEO sets up all three for one organization in thirty days, with your team in the room, and hands over the process on day thirty.
The engagement is applied to your site by the person who built the method: with your developers, your content owners, and your analytics in the loop, and with the parts no checklist handles on its own — the audit of what AI actually says about you today, the prioritization of what moves the needle first, the fixes your platform makes awkward, the agent-readiness layer, and the measurement that proves it moved.
Generative engine optimization
Six engines are building their answers right now. Most of them can't reach your site.
When someone asks ChatGPT which accounting firm to use, or asks Perplexity which project management tool handles remote teams, or asks Google's AI Overview which attorney handles employment disputes in their city — those engines go looking for a page to cite. They send bots, they crawl, they read, they pull sentences. The businesses they name are not always the best. They are the ones the engines could read. Most websites silently block at least one of those bots without knowing it. Most pages give the engine nothing quotable. Most businesses describe themselves differently in every directory they appear in, so no engine can build a confident entity match. GEO fixes all of that — systematically, page by page, engine by engine.
The Five Elements of GEO
CRAWL ACCESS
The most common GEO failure is not bad content. It is a firewall rule, a Cloudflare setting, or a JavaScript render requirement that silently blocks the bots ChatGPT, Perplexity, and Gemini send. If the bot cannot read the page, the engine cannot cite it. The audit starts here.
INDEX PRESENCE
ChatGPT and Microsoft Copilot answer from Bing's index, not Google's. A top Google ranking does not transfer. If your pages are not in Bing's index, you are invisible to two of the six major AI surfaces regardless of your Google position.
ENTITY CONSISTENCY
AI engines build knowledge graphs. Your business needs one consistent entity description: the same name, category, location, and summary across your site, your Google Business Profile, every directory you appear in, and every page that mentions you. Inconsistency tells the engine the business is not authoritative enough to cite with confidence.
QUOTABLE CONTENT
AI engines lift sentences whole. A page of brand voice and marketing language gives them nothing they can extract cleanly. The sentences that get cited are specific, self-contained, and state one verifiable fact. The answer-first paragraph — the one that answers the question in the first two sentences — is the paragraph the engine quotes.
FRESHNESS SIGNALS
AI engines weight recency. A page last updated three years ago ranks below a page updated last month, all else equal. Freshness signals are readable: the `dateModified` field in your Article schema, a visible publication date, and content that references current conditions.
What GEO-ready page schema looks like
JSON-LD
{
"@context": "https://schema.org",
"@type": "Article",
"headline": "How to choose an employment attorney in New York",
"author": {
"@type": "Person",
"name": "Jane Smith",
"url": "https://yourfirm.com/team/jane-smith"
},
"publisher": {
"@type": "Organization",
"name": "Your Firm Name",
"url": "https://yourfirm.com"
},
"datePublished": "2025-09-01",
"dateModified": "2026-10-15",
"mainEntityOfPage": {
"@type": "WebPage",
"@id": "https://yourfirm.com/blog/choose-employment-attorney-new-york"
},
"description": "Five questions to ask before hiring an employment attorney in New York, with guidance on fee structures, case types, and response time expectations."
}This is what a GEO-ready page tells an AI engine: who wrote it, when it was last updated, and what question it answers. Missing fields — especially `dateModified` and `author` — are the reason an engine cites a competitor's older, weaker page instead of yours.
The GEO layer of the Sprint audits every crawl block, every Bing index gap, every entity inconsistency, and every page where AI engines are citing competitors instead of you. The fixes are applied in the first two weeks. The day-30 measurement shows which surfaces moved.
Answer engine optimization
AI engines give one answer. The business that gets cited wrote that answer first.
There is a structural difference between a page that gets cited as an answer and a page that does not. It is not word count. It is not domain authority. It is whether the page is built the way an AI engine expects an answer source to be built — with the answer at the top, the question in the heading, the supporting facts in the right order, and the markup that tells the engine this page is designed to be quoted. Most pages are written for humans who will scroll and scan. AEO restructures the pages that matter so the engine can read them as a source the first time it looks.
The Five Elements of AEO
ANSWER-FIRST STRUCTURE
The first paragraph of an AEO-optimized page states the answer in full, then supports it. Most pages do the reverse: three paragraphs of context, then the answer. An AI engine processing thousands of pages selects the one that answers first.
QUESTION-FIRST HEADINGS
AI engines match search queries to headings. "Our Shipping Policy" does not match "how long does shipping take." "How long does shipping take?" matches exactly. The difference between being cited and being skipped is often one heading rewrite per page.
STRUCTURED MARKUP
FAQPage, HowTo, Article, and Speakable schema tell an engine which content is structured as an answer source. A page with FAQPage schema is readable as a Q&A source without inference. A page without it makes the engine decide — and it usually decides on something else.
AUTHORITY SIGNALS
Bylines with linked author pages, visible publication and update dates, outbound links to primary sources. An AI engine asked to cite a page favors content it can attribute to a credible, identifiable source. Anonymous content — no author, no date, no citations — is passed over.
CITATION SURFACE
The pages that get cited most are not always the most comprehensive pages on a site. They are the pages already referenced externally: linked from credible sites, mentioned in industry threads, cited in reviews. AEO includes the off-site work that builds citation surface, not only the on-page work.
What AEO-ready FAQ markup looks like
JSON-LD
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [
{
"@type": "Question",
"name": "How long does implementation take?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Implementation takes thirty days. Week one is the audit and baseline. Weeks two and three are the fixes. Week four is the operating system, measurement, and hand-off."
}
},
{
"@type": "Question",
"name": "Do you work with enterprise teams?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Yes. Enterprise engagements with multiple brands, regions, or sites are scoped on the discovery call. The base Sprint covers one organization; larger scopes are priced accordingly."
}
}
]
}This is the markup that tells an AI engine your page is structured as an answer source. Without it, the engine reads your Q&A content as body text. With it, each question and answer pair is individually citable.
The AEO layer of the Sprint restructures your highest-value pages — the ones buyers land on when they are deciding — so they are built the way an AI engine expects an answer source to look. The heading audit, the answer-first rewrites, the FAQ blocks, and the structured markup are all delivered as part of the implementation sprint in weeks two and three.
Agentic commerce optimization
The buying loop is changing. ACO is how you stay in it.
Traditional search optimizes for clicks. Agentic commerce optimization (ACO) optimizes for selection. When a human searches for "best running shoes," they browse a list of links. When an AI agent searches for "waterproof running shoes under $150 that fit wide feet and can deliver by Friday," it runs rigorous logical parameters against every data point it can read — and returns one answer. If your product data is incomplete, hidden behind JavaScript, or missing from your schema, your brand does not appear in that answer. It does not even appear as a runner-up. It does not exist.
The Five Pillars of ACO
COMPLETENESS
Granular product schema in JSON-LD: SKUs, GTINs, variants, exact dimensions, prices, availability and shipping details. An agent cannot evaluate what it cannot read.
CONTEXT
Conversational attributes alongside standard fields: "compatible accessories," "weather operating limits," "requires professional installation." AI agents match on specifics, not categories.
CITATIONS
Third-party validation the AI models trust: editorial reviews, verified aggregate ratings, forum mentions. An agent that cannot confirm your claims skips you for a source it can.
CORRECTNESS
Absolute data integrity across product feeds. A price mismatch or an out-of-stock flag in the wrong field causes an agent to filter you out before any human is involved.
CONVERSION PATH
Friction-free checkout paths machines can navigate: programmatic payment authorization, virtual card support, stable non-JavaScript forms, and a `native_commerce` feed attribute that signals agent-readiness to the protocol layer.
What agent-ready markup looks like on a product page
JSON-LD
{
"@context": "https://schema.org",
"@type": "Product",
"@id": "https://yourstore.com/products/trail-pack-45l",
"name": "TrailMaster 45L Ultralight Pack",
"sku": "TM-45L-GRN-M",
"gtin13": "0123456789012",
"description": "Ultralight 45L daypack. Frame: carbon-fiber stays. Weight: 1.1 kg. Max load: 18 kg. Weather rating: IPX4 splash-resistant. Compatible with 3L hydration bladders.",
"brand": { "@type": "Brand", "name": "TrailMaster" },
"offers": {
"@type": "Offer",
"price": "189.00",
"priceCurrency": "USD",
"availability": "https://schema.org/InStock",
"priceValidUntil": "2026-12-31",
"shippingDetails": {
"@type": "OfferShippingDetails",
"shippingRate": { "@type": "MonetaryAmount", "value": "0", "currency": "USD" },
"deliveryTime": {
"@type": "ShippingDeliveryTime",
"handlingTime": { "@type": "QuantitativeValue", "minValue": 0, "maxValue": 1, "unitCode": "DAY" },
"transitTime": { "@type": "QuantitativeValue", "minValue": 2, "maxValue": 5, "unitCode": "DAY" }
}
}
},
"additionalProperty": [
{ "@type": "PropertyValue", "name": "weatherRating", "value": "IPX4" },
{ "@type": "PropertyValue", "name": "compatibleWith", "value": "3L hydration bladder" },
{ "@type": "PropertyValue", "name": "agentReadiness", "value": "ACP-1.0" }
],
"aggregateRating": {
"@type": "AggregateRating",
"ratingValue": "4.8",
"reviewCount": "312"
}
}This is what an AI buying agent reads before it selects a product. Missing fields — shippingDetails, GTIN, conversational attributes — are the reason it skips to the next result.
The ACO layer of the Sprint delivers this for your store: the markup for your product template, the feed rules that push the right data to every variant, the exceptions list, and a verified sample of live pages. For catalogs above 1,000 products, this is delivered as a specification — your developer applies it once at the template level and it propagates to every product page. No manual page-by-page editing at scale.
Counted, so you can hold us to it
Eight deliverables. Thirty days. One fixed price.
The AI Visibility Audit (week 1)
Most teams discover on day one that they are invisible to at least two of the six AI surfaces their buyers use. The audit names every gap, every block, and every competitor being cited in their place — with the exact fix for each failure. Six engines covered: ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini and Copilot. Scored against the 20-point MeetGEO standard, page by page, for up to 25 commercially important pages.
The Implementation Sprint (weeks 2–3)
The twenty fixes applied to your site with your team: crawler access and the firewall rules that silently block AI; Bing and the indexes ChatGPT and Copilot search; text that lives in the raw HTML, not only in scripts; one consistent entity description everywhere you are listed; answer-first opening paragraphs; headings written as customers ask; quotable, self-contained sentences; comparison tables; sourced numbers; FAQ blocks on the pages that sell; directory and listing presence; the community threads AI reads; review structure; verifiable bylines; fit statements; freshness signals; images AI can read; comparison and versus pages; a prompt test bank; and a measurement setup. Delivered as a tracked list — each item done, handed to your developer with exact instructions, or marked not applicable with the reason.
Agent Readiness / ACO Layer (week 3)
Your prices, availability, service area, policies and product facts expressed in the structure an AI buying agent reads before it acts — covering the five ACO pillars: Completeness (full product schema with GTINs, variants, shipping details), Context (conversational attributes agents match on), Citations (third-party validation structure), Correctness (feed integrity across all variants), and Conversion path (programmatic checkout compatibility). For e-commerce catalogs this is a specification — the markup for your product template, the `native_commerce` feed rules, the Model Context Protocol (MCP) and Agentic Commerce Protocol (ACP) readiness checklist, and a verified sample of live pages. Your developer applies it once; it reaches every product.
The Operating System (week 4)
The processes that keep the work running after we leave: owners named for each of the twenty checks, a monthly 45-minute review ritual with its agenda, a quarterly prompt-test routine with the exact prompts and scorecard, a page-publishing checklist your content team runs before anything goes live, and a 90-day plan with dates. Written for your team, not for us.
Measurement you can show your board
A baseline on day one and a reading on day thirty across the same six AI surfaces and the same prompt bank, so the engagement is judged on movement, not activity. Plus the dashboard definitions and the tracking that keeps the numbers updating after we leave. AI traffic does not appear in your analytics labeled "AI" — we set up the custom channel grouping that surfaces it correctly, because most businesses currently have this traffic hidden in "Direct."
Team training (weeks 1–4)
Four recorded sessions: a 90-minute kickoff for marketing, content, web and leadership that explains the new rules in plain language and gets the decisions made; two 60-minute working clinics in weeks 2 and 3 where your content and web owners apply fixes on real pages with us; and a 90-minute hand-off that walks each owner through their checklist. Recorded, so the next hire gets the same briefing.
Content strategy and templates (week 4)
A 90-day content plan built on what the audit found: the questions your buyers ask that nobody answers well, the pages to write first, the comparison and versus pages your category is missing, and a monthly cadence your team can hold. Delivered with the templates to execute it: the answer-first page template, the FAQ block, the comparison table, the product or service page structure, the author and about-page pattern, and the pre-publish checklist — each with a worked example from your own site.
Sixty days of access
Questions by email for sixty days after day thirty, answered within one business day. The first time your developer hits a strange case is in week six, not week two, and the answer matters more then than it does during the Sprint itself.
Week by week
The thirty days
Week 1
See what AI sees
Day 1 baseline across six surfaces and your prompt bank. Full crawl and block audit: which bots are allowed, which are silently blocked, which indexes are missing. Entity and listing audit. The written report and a prioritized fix list by Friday, reviewed in the kickoff session.
Week 2
Open the doors and fix the foundations
Crawler and firewall access. Bing and the indexes ChatGPT and Copilot read. Raw-page text that exists without JavaScript. One consistent entity description everywhere. Structured business and organization data. These are the fixes that decide whether anything else can work.
Week 3
Become the answer, and the candidate
Answer-first rewrites of your top pages. Headings, quotable sentences, tables, sourced numbers, FAQ blocks. Listings, threads, reviews, bylines, fit statements, freshness, images, comparisons. The ACO layer: product schema, feed rules, agent-readiness checks for MCP and ACP compatibility.
Week 4
Make it run without us
Owners, rituals, the publishing checklist, the prompt-test routine, the 90-day content plan and the templates. Day 30 reading against the day 1 baseline. Hand-off session. Sixty days of email access begin.
Fit
Who this is for
YES
A marketing director who has been asked "why aren't we in ChatGPT?" and does not want to guess. A business with a website that already works and a team that can make changes within days, not quarters. An e-commerce brand whose products are sold or compared online and whose buyers use AI to shortlist before they buy. An agency that wants the method installed in one client account first, with the process documented so it can be repeated. A category where buyers ask questions before they buy: services, professional firms, software, local businesses with real competition, anything booked or compared.
NO
A site that needs rebuilding before it can be improved. A team with no developer or content owner available for the thirty days. A brand that wants a report filed and no changes made. A business whose customers never ask an AI anything.
Investment
What it costs, and why it is priced this way
From$20,000Fixed. No retainer.
One organization · 30 days · up to 25 commercially important pages
The base Sprint covers one organization, up to 25 commercially important pages and one product or service template, in thirty days. E-commerce catalogs above 1,000 products, multiple brands or regions, and multi-site groups are scoped on the discovery call — the ACO specification work scales differently from page-by-page edits, and the price reflects the scope. Whatever the engagement, the price is fixed before we start.
Before you evaluate this number, name the deal you are not closing because the AI names a competitor instead. Name the enterprise contract, the franchise account, the repeat buyer who found someone else through a question they asked an assistant. A single one of those — one month of bookings, one retained client, one contract — pays for the Sprint. The process it leaves behind keeps paying every month after. There is no retainer because we do not want one: an engagement that installs a dependency rather than a capability is an agency model, not this one.
An agency keeps the method and bills it every month. The Sprint sells it once and leaves the method with your team, with named owners, documented rituals and the measurement to prove it runs.
One organisation at a time · Book to check availability
Why MeetGEO
The method is public. The application is not.
Deb Fiorentino built MeetGEO and has spent years auditing how AI engines read, cite and act on websites across GEO, AEO and ACO. The fixes are not the hard part. Knowing which ones your site fails, in what order, with what your platform allows, getting them done inside a real organization in thirty days, and building the process that survives after the engagement ends — that is the hard part.
You get Deb, not an account manager. The person who ran the audits the series is drawn from, who understands what ACP and MCP mean for your checkout flow and what they mean for your product template, and who will still be answering questions by email in week eight when the edge cases arrive.
Common questions
Questions people ask on the discovery call
Will we see results in thirty days?
You will see the day 30 reading against the day 1 baseline on the same prompts and surfaces. AI surfaces re-read pages on their own schedules, so some movement lands in the weeks after; the sixty days of access and the prompt-test routine exist for that reason.
Do we need to stop our SEO work?
No. Everything here makes your pages better for Google too, and nothing here undoes Google work. The two disciplines share pages and split owners.
What does our team have to do?
Make changes within days when they are handed over with exact instructions, attend a 90-minute kickoff, two 60-minute working clinics and a 90-minute hand-off, and name an owner for each check by week four. If a fix needs a developer, we write the ticket; your developer does the work.
What if our site is on a platform that limits what we can change?
Most of the twenty checks are content and configuration, not code. Where a platform blocks a fix, the report says so, offers the nearest alternative, and the owner's checklist records it so it is not forgotten.
Can an agency buy this for a client?
Yes. The hand-off documents are written to be reused, and the process is the part an agency keeps.
How do we start?
Book the discovery call. If it is a match, you receive a one-page agreement, a start date within two weeks, and the day 1 baseline on the morning we begin.
Your next customer is asking an AI right now.
Thirty days from your start date, the answer includes you.
Prefer email? deb@meetgeo.ai