Answer engine optimization (AEO) is the work of making your business findable, understandable and citable for the AI tools that now answer buyers' questions directly. The fundamentals are the same for everyone. What changes by business type is where the answers live and which data the engines trust.
Below is how we would build AEO for a local service business, an online seller and a B2B or government contractor. If you are new to the term, start with our guide to what answer engine optimization is.
The foundation every business shares
Start with what the engines have actually published. Google says there are no extra requirements to appear in AI Overviews or AI Mode. A page must be indexed and eligible to show a snippet, the same bar as regular search. So AEO on Google is good SEO done well, not a separate game.
The other engines publish their own crawler rules. OpenAI says sites that block OAI-SearchBot will not be shown in ChatGPT search answers.
Anthropic runs three crawlers: ClaudeBot for training, plus Claude-SearchBot and Claude-User for search and live answers. Blocking a training crawler is a separate decision from blocking a search crawler, and it is easy to mix the two up in robots.txt. Google-Extended controls whether Gemini can use your content for training and grounding, and Google says it does not affect inclusion in Google Search.
Two more facts shape the architecture. Google's structured data rules say markup must describe what readers can actually see on the page, so markup that promises more than the page shows works against you. And Google has stopped showing FAQ rich results, so FAQ markup is no longer a shortcut to extra space in search results.
Every architecture below sits on four layers:
- Access. Search crawlers can reach your pages. You decide on training crawlers separately.
- Entity. One consistent description of who you are, what you sell and where.
- Answers. Pages that answer the real questions buyers ask, in the first lines.
- Proof and measurement. Evidence the engines can cite, and a way to see what changed.
The layers stay the same for every business, but the weight on each one changes with the business model.
Archetype 1: The local service business
Think plumber, dental office, law firm or HVAC company, where most buyers ask a question with a place in it.
Where the answers come from. Google says local results depend mainly on relevance, distance and prominence. Review count and review score feed prominence, so for this business the Google Business Profile does much of the work the website does for other types.
The architecture we would build:
- Entity layer first. A complete Google Business Profile with the right primary category. Name, address, phone and hours match exactly across the profile, the website and the major directories.
- Answer layer. One page per service, with the service area named in it. Each page answers what a caller asks on the phone: what the job includes, how scheduling works, what affects the price, and whether you handle emergencies. If the answer lives only in your head, an answer engine cannot repeat it.
- Proof layer. A steady habit of asking for reviews and replying to every one. Real job photos on the profile and the site.
- Markup. LocalBusiness markup that matches what the page shows, and nothing it does not.
- Measurement. Calls and form fills by source, plus the Search Console queries that name your town.
A gap to check: the website says "contact us for a quote" on every page, and answer engines have nothing to quote from that. Say what drives the price, even if you can't print a number.
Archetype 2: The online seller
Think a Shopify store, an Etsy shop with its own site, or a brand selling a catalog direct.
Where the answers come from. Shopping questions are about specific products, so structured product data matters most here. Google's shopping experiences draw on product data from Merchant Center, and free listings use the same data at no cost, so you don't need an advertising budget to be eligible.
The architecture we would build:
- Feed layer first. Merchant Center with free listings turned on. Complete attributes: title, brand, GTIN where the product has one, price, availability, shipping and return policy.
- One source of truth. Product pages and their markup pull from the same data as the feed. Keep price and availability identical in the feed and on the page, so the engines never see two answers for the same product.
- Answer layer. Specs written as text, not locked in images. Plain-language policy pages for shipping, returns and sizing. Buying guides that answer "which one is right for me" honestly, including when a product is the wrong fit.
- Access layer. OAI-SearchBot, Claude-SearchBot and PerplexityBot allowed, so ChatGPT, Claude and Perplexity can reach your product pages when they build answers.
- Measurement. Free-listing clicks in Merchant Center, organic sessions to product pages, and orders by landing page.
A gap to check: a store that has never opened the Free listings page in Merchant Center, or has left product attributes incomplete.
Archetype 3: The B2B firm or government contractor
Think a consulting firm, a software vendor selling to businesses, or a small contractor chasing federal set-asides. Buyers in this group usually vet a firm before they reach out, so the public site has to answer their evaluation questions.
Where the answers come from. Evaluation questions: who does this work, how they price it, how long it takes, and whether they have done it before. For federal work, SAM.gov registration is generally required to receive an award, so buyers expect to see your identifiers.
The architecture we would build:
- Entity layer first. An About page that states plainly who you are, what you do, who you serve and where. Organization markup that matches it. For contractors, the identifiers buyers check, such as UEI, CAGE code, NAICS codes and certifications, appear on one capability page in text form.
- Answer layer. Answer-first explainers for each question in the evaluation: your process, typical timelines, how pricing works, and what a first engagement looks like. Each opens with the answer, then the detail.
- Proof layer. Case studies that name the mechanism ("replaced the weekly manual report with a scheduled pull"), not vague outcomes. Only claims you can back with evidence.
- Topic cluster. Explainers linked to each other and to the service pages they support, so an engine can see you cover the subject end to end.
- Measurement. Search Console queries by topic, contact-form leads by landing page, and a monthly check of how AI answer tools describe your firm.
A gap to check: expertise that lives in sales decks and proposals rather than on the public site, because the engines can only cite what they can read.
What this looks like side by side
| Layer | Local service | Online seller | B2B or contractor |
|---|---|---|---|
| Heaviest lift | Business Profile and reviews | Product feed and data parity | Entity page and explainers |
| Answer pages | Service by area | Specs, policies, buying guides | Process, pricing model, timelines |
| Proof | Reviews and job photos | Ratings, policies, accurate stock | Mechanism-level case studies |
| Key metric | Calls and forms by source | Free-listing clicks and orders | Leads by landing page |
Where to start
Check access first: open your robots.txt and your firewall or CDN bot settings, confirm the search crawlers are allowed, and make your training-crawler choice on purpose. Next, rewrite the opening lines of your five most-visited pages so each one answers its question directly. Then build out the heavy layer for your type.
Every business has quirks that move these layers around. A clinic has different rules from a contractor, and a three-product store differs from a 5,000-item catalog. Send us a message for tailored solutions, and tell us your business type and what you want buyers to find. We will reply with the architecture we would build for you.
Want a scored baseline first? Our Agent Readability Audit checks how AI agents see your site today and gives you a prioritized fix list.