Structured Data Types That Improve AI Citations

Most guides on schema markup for AI visibility stop at LocalBusiness. That's a mistake — not because LocalBusiness isn't important, but because it's the floor, not the ceiling.

AI retrieval systems are reading multiple schema types on every page they index. If your competitors have five schema types and you have one, they're giving AI models significantly more signal to work with. Here's what actually moves the needle beyond the basics.

FAQPage schema

FAQPage is the schema type most directly aligned with how AI models answer questions. When a user asks ChatGPT or Perplexity something like "does [business type] offer same-day appointments," the AI is looking for structured Q&A content it can retrieve with confidence.

Adding FAQPage schema to your service pages or FAQ section makes your answers machine-readable. The AI doesn't need to parse prose and guess your position — it reads the question and answer directly from your markup.

The questions should match real queries. "Do you offer emergency plumbing repairs?" performs better than "What services does our company provide?" Think about the specific questions your customers ask before booking, then answer them in FAQ schema.

Aim for four to eight questions per page. More than that starts to dilute the signal.

Service schema

Service schema is one of the most consistently underused types we see in our Signal Check audits. Most businesses describe all their services in a single paragraph of body copy. AI models retrieve entity information — they don't parse marketing prose well.

With Service schema, each offering becomes a discrete, structured entity with a name, description, area served, and provider. When someone asks Perplexity "who does kitchen renovations in Oakville," a business with Service schema for kitchen renovations in that city is a much stronger match than one where the service only appears as body text.

Implement separate Service schema blocks for each major offering. Each block should include `name`, `description`, `provider` (linked to your LocalBusiness entity), and `areaServed`. If you have ten services, create ten blocks.

AggregateRating schema

AI models treat review data as a trust signal, but only if the rating is structured and machine-readable. A hundred Google reviews you haven't marked up in schema are less visible to AI retrieval than twenty reviews embedded in AggregateRating schema on your site.

AggregateRating sits inside your LocalBusiness or Service schema. It requires `ratingValue`, `reviewCount`, and `bestRating`. Keep these numbers accurate and current — AI platforms do cross-reference against live review sources, and mismatches reduce trust.

One common mistake: marking up ratings on pages where the reviews aren't actually displayed. Don't do this. Google's quality signals and AI retrieval systems flag schema that doesn't correspond to visible content.

Article and BlogPosting schema

If you publish educational content — guides, how-to posts, explainers — Article or BlogPosting schema turns that content into citable sources rather than just readable pages.

AI models have a strong tendency to cite structured content when answering informational queries. A blog post about "how to spot a failing water heater" with proper Article schema, a clear author entity, and a publication date is far more likely to be cited by Claude or Perplexity than the same content without markup.

The fields that matter most here: `headline`, `author` (with a linked Person entity), `datePublished`, `dateModified`, and `description`. The `dateModified` field is particularly important — it tells AI models whether your content is current.

HowTo schema

HowTo schema is purpose-built for procedural queries, and AI models love procedural queries. "How do I know if I need a new roof?" "How does the custom kitchen design process work?" These are exactly the kinds of questions that drive people to AI platforms.

Implementing HowTo schema on the right pages is a direct pipeline from a user's how-to question to your business. Each step is structured with a `name` and `text`, and the overall schema includes a `name` (the how-to title) and `description`.

The best candidates for HowTo are service process pages ("How our consultation works"), diagnostic guides ("How to tell if your furnace needs repair"), and any page where you explain steps a customer takes. Don't force it onto pages that aren't procedural — the match has to be genuine.

How to layer these types without breaking anything

Multiple schema types coexist as separate JSON-LD blocks. You don't merge them into one object — each type has its own `<script type="application/ld+json">` block in the page head.

A well-structured service page might have: one LocalBusiness block (or a reference to a site-wide one), one Service block specific to that page, one FAQPage block with questions relevant to that service, and an AggregateRating block if reviews are displayed. That's four clean, separate blocks — AI models parse all of them.

Test every schema implementation with Google's Rich Results Test before deploying. Invalid JSON breaks the whole block. A syntax error on the AggregateRating block won't affect your FAQPage, but you still don't want garbage markup sitting in your page source.

Where most businesses still have gaps

The pattern we see consistently in Signal Check audits: businesses implement LocalBusiness schema correctly, then stop. The sites with the strongest AI citation rates have three to five schema types deployed across their key pages — not just the homepage.

If you're not sure which types you're missing or whether your current implementation is valid, run a free Signal Check at sourcepull.ca. The audit flags missing types, invalid markup, and the specific gaps that are likely holding down your citations across ChatGPT, Perplexity, Claude, and Gemini.

Schema is infrastructure. It's not glamorous, but it's what separates a business AI can cite confidently from one it ignores.