Schema.org for AI: how structured data gets your app cited

How AI models use structured data

When ChatGPT, Perplexity or Google's AI answers cite your app in response to a question like "what's a good tool for splitting expenses," the model is working from two things: retrieval results (pages it found in its search index) and structured data on those pages. The retrieval gets your page into the running; the structured data tells the model exactly what it is.

Without structured data, the model reads your homepage prose — which is written to convert visitors, not to answer a factual query. With it, the model has a clean set of facts: name, description, category, pricing, author. Those facts map directly to the kinds of questions users ask AI assistants.

The three schemas that matter for AI

1. Organization

Tells the model who built the app — the legal name, the brand, the social profiles, the contact point. This is the schema that disambiguates your app from any other company with the same name and gives the model provenance to cite.

"@type": "Organization",
"name": "Actuant",
"url": "https://www.actuant.dev",
"description": "The launch & growth copilot for people who build with AI."

2. SoftwareApplication

The most important schema for an AI citation. It tells the model your product is an application (not a blog or a company page), what category it belongs to, and what it costs. The applicationCategory field maps to a Schema.org taxonomy value like DeveloperApplication or FinanceApplication — this is the field a model uses to match your app to a category query.

"@type": "SoftwareApplication",
"name": "Tabby",
"applicationCategory": "FinanceApplication",
"operatingSystem": "Web",
"description": "Split shared expenses with friends, no signup for guests.",
"offers": { "@type": "Offer", "price": "0", "priceCurrency": "USD" }

3. FAQPage (if you have an FAQ)

If your site has an FAQ section, marking it up as FAQPage with Question and Answer items gives the model ready-made Q&A pairs. AI assistants are especially likely to cite FAQ content because it matches the question-answer format of the prompts users type.

How this differs from SEO structured data

For Google's blue-link results, structured data powers rich snippets — star ratings, pricing, breadcrumbs. For AI, structured data powers understanding — the model's ability to say "Tabby is a free finance app for splitting expenses" rather than "there's a website called Tabby that mentions splitting expenses." The same JSON-LD block serves both, but the AI use case puts more weight on description, applicationCategory and offers than on the fields that produce visual rich results.

What AI assistants can't do (yet)

Structured data helps a model that has already retrieved your page. It doesn't help your page get retrieved in the first place — that depends on being crawlable, being linked from pages the model's retriever trusts, and having content that matches the kinds of questions users ask. Structured data makes your facts unambiguous; it doesn't generate the retrieval itself.

How to verify

1. 1

   Google Rich Results Test

   search.google.com/test/rich-results — paste your URL. Confirm the Organization and SoftwareApplication schemas are detected and valid.

2. 2

   View Page Source (Cmd+U)

   Search for application/ld+json. Confirm the JSON-LD is in the raw HTML, not injected by JavaScript.

3. 3

   Ask the assistant about your app

   Prompt ChatGPT with web search: "what is [your app]?" If it can describe your app accurately, the structured data and crawlable content are working together.

Actuant checks for JSON-LD in your raw HTML, validates the Organization and SoftwareApplication schemas, and reports missing fields that AI assistants use for categorization and citation.