Helping search engines understand your content with precise schema markup.
A structured approach to schema — diagnosing where your markup is missing or broken, engineering a complete validated schema architecture, and compounding rich-result and AI-citation visibility.
// 01 — DIAGNOSE
We test your existing structured data against Google's Rich Results Test and the Schema Markup Validator — finding missing types, incomplete required properties, and the validation errors that quietly block rich results.
→ DELIVERS: SCHEMA_GAP_MAP
// 02 — ENGINEER
A complete JSON-LD architecture matched to your content type — Organisation, Article, FAQPage, Product, BreadcrumbList and more — every type validated before it ships.
→ DELIVERS: SCHEMA_STACK
// 03 — COMPOUND
Rich-result impression share and AI citation rates — tracked monthly via Search Console, with schema errors caught and fixed as CMS and theme updates introduce them.
→ DELIVERS: RICH_RESULT_TRACKING
We build the technical bridge between your content and the LLMs that consume it, using advanced schema architectures.
We assess where you're being found, where you're invisible, and what's costing you leads — before you commit to anything.
Every page asks a search engine — and now an AI model — to work out what it is. Without structured data, that's inference: the machine reads your prose and guesses. With it, the facts are declared. Inference is fragile. Declaration is not.
Structured data — JSON-LD — is a layer of machine-readable fact sitting alongside your content: who wrote a page, what type of content it is, which entity it represents, what questions it answers. Google uses it to render rich results. AI systems use it to verify claims and decide what's safe to cite.
It doesn't move you up the rankings on its own. What it does is remove ambiguity — and ambiguity is exactly what costs you a rich result, an AI citation, or a correctly attributed brand fact. The less a machine has to guess, the more it can give you.
→ Content is read · schema is understood
A page has a content layer a human reads. Structured data adds a second layer a machine reads. Same page — but one of them leaves nothing to interpretation.
// .with_and_without_schema
What a page looks like to a machine, with and without a schema layer
↓ WHAT THE MACHINE HAS TO INFER
MACHINE INTERPRETATION
// JSON-LD — THE DATA LAYER
{ "@type": "BlogPosting", "author": "Paul Gordon", "datePublished": "2026-05-12", "about": "BeFound Search" }
↓ WHAT THE MACHINE NOW READS
MACHINE INTERPRETATION
→ SAME WORDS · DIFFERENT CERTAINTY — INFERENCE LEFT, DECLARATION RIGHT
The content layer is for people. The structured-data layer is for the machines deciding whether to show you a rich result or cite you in an AI answer. Skip it and you're asking those systems to infer everything — author, type, entity, freshness — from prose alone.
We build the second layer in JSON-LD: Google's recommended format, sitting in the page head, separate from your design and simple to maintain.
→ Declare the facts · don't leave them to be inferred
Schema isn't one thing — it's a vocabulary. Each type declares a specific kind of fact, and each unlocks a specific result in search or AI. You implement the types your content actually earns.
// .type_declares_unlocks
Five core schema types — and what each one unlocks
→ ONE VOCABULARY · MANY DOORS — FAQPAGE OPENS THE AI ONE
There's a baseline every site should have — Organisation, Article or BlogPosting, BreadcrumbList — plus type-specific markup for what you actually do: Product for e-commerce, LocalBusiness for local, FAQPage anywhere you answer questions.
FAQPage is the standout. Q&A content marked up in machine-readable form is pulled into AI Overviews at a markedly higher rate than the same content as plain prose — it's already in the shape the retrieval systems are looking for.
→ The right types · validated · monitored in Search Console
Questions about this service, answered directly.