Visible facts, rendered markup

Structured Data & Visible-Content Validation

The structured-data layer should represent the facts and relationships on the page with the smallest valid graph the evidence supports.

Our specialists inspect the rendered JSON-LD beside the page, trace mismatches to the template that produced them and check the approved graph again after deployment. Content and engineering owners who need valid markup to remain aligned with the page through the next deployment.

Your structured data contains only facts a visitor can verify on the page. Unsupported claims stay out of the graph.

Specialist checking structured data markup against the visible page content it describes

Some of the 500+ brands we've worked with

See all references
  • Decathlon
  • Mynet
  • Gedik Yatırım
  • Jack Martin Menswear
  • Adore Mobilya

Five gates before release

Five gates, and a named owner at each one

Named specialists interpret the page, own implementation and accept the final result. The collecting, comparing and drafting all happen beneath those decision gates.

How we hold ourselves to it

  • Markup mirrors the page
  • Remove unsupported claims
  • Production is the real test
  • No rich-result promises

  1. Map page purpose to eligible markup

    Content and technical SEO specialists inventory the templates, define each page's purpose and map it to schema types and properties allowed by current official guidance.

    Page-to-schema eligibility matrix with explicit excluded types and reasons.

    AI assist
    Using the page inventory, the evidence retrieval agent proposes supported schema types from template purposes, visible page jobs and official eligibility requirements.
    Human gate
    Content, product and technical SEO owners decide the page purpose, eligible types and explicit exclusions.

  2. Compare visible facts with current JSON-LD

    A specialist checks rendered JSON-LD on representative URLs against visible names, dates, authors, offers, identifiers and relationships.

    Visible-fact parity register with missing, conflicting and unverifiable properties.

    AI assist
    For each representative URL, the evidence retrieval agent parses rendered JSON-LD and prepares field-level diffs against visible names, dates, identifiers, claims and relationships.
    Human gate
    The accountable content or entity owner confirms the authoritative value for every material mismatch.

  3. Remove unsupported and duplicate claims

    Engineering owners get an exact map of duplicate nodes and unsupported claims across the CMS, components, plugins and integrations before anything is removed.

    Markup ownership and defect map with safe removal boundaries.

    AI assist
    Before anything is removed, the evidence retrieval agent identifies unsupported properties and duplicate nodes while preserving required identifiers and dependencies.
    Human gate
    Technical SEO, content and engineering owners sign off on each removal and the templates it affects.

  4. Specify minimal entity relationships

    We write the smallest connected entity graph that carries the approved page facts, using only supported relationships and eligibility.

    Minimal template-level JSON-LD specification with identifier and nesting rules.

    AI assist
    For the graph specification, the evidence retrieval agent drafts stable identifiers, supported properties, relationship direction and visible-copy requirements.
    Human gate
    Entity, privacy, content and engineering owners set the facts, publication boundary and implementation contract.

  5. Validate, release and monitor

    A specialist checks syntax, required properties, canonical identifiers and visible parity on rendered fixtures. After release, the deployed graph is inspected again, with fresh checks after template, content-model or entity changes.

    Pre-release validation pack, then a production parity and regression ledger with accountable remediation owners.

    AI assist
    The independent validation agent renders candidate markup, runs representative and edge-case fixtures, schedules production checks and opens regressions without inferring ranking impact.
    Human gate
    A technical SEO specialist and engineer decide whether production is ready. The web owner triages regressions, and an independent reviewer verifies closure.

  6. Finding classifications and source limits

    The audience-question map and visible-content parity checks are Observed, along with classic snippet and AI answer captures kept as downstream context only. Sampled template-parity coverage counts as a Proxy. Official schema eligibility guidance is a Constraint. A markup change stays a Hypothesis until production parity gets replayed.

    How strongly each markup finding is evidenced, and the constraint its source puts on it.

    AI assist
    Classifies each finding by the evidence behind it and records the source limits that keep a diagnosis from being stated as certainty.
    Human gate
    The technical SEO specialist approves each classification and rejects any rich-result claim the validation cannot support.

The release record

A graph another specialist can reproduce

Each handoff names its owner, the decision it supports and the provenance another specialist would need to challenge the result.

  • Structured-data audit

    A rendered-page audit of invalid syntax, unsupported properties, duplicate identities and visible-content mismatches.

    Accepted when

    Determines what to remove, repair, consolidate or leave unchanged before adding any new markup.

    Cadence: Pre-add audit

  • Minimal markup implementation specification

    A template-specific graph contract defining eligible types, properties, identifiers, nesting and visible evidence requirements.

    Accepted when

    Gives engineering and content teams one minimal implementation target without promising enhanced visibility.

    Cadence: Single target set

  • Template-level validation fixtures

    Executable representative and edge-case fixtures for syntax, identifier and parity validation in the release flow.

    Accepted when

    Defines whether a markup change is safe to ship and which failures block release.

    Cadence: Ship gate tested

  • Production parity monitoring report

    A deployed-page report showing current validity, parity, duplicate-node and regression status by template.

    Accepted when

    Supports repair and ownership decisions when production output diverges from the approved specification.

    Cadence: Drift monitored

  • Release sign-off rule

    Production markup must validate and remain aligned with visible approved facts. Rankings, recommendations, referrals and conversions are measured separately, so this rule makes no ranking or citation promise.

    Accepted when

    A finding counts once it is reproducible, evidence-labeled, human-approved and checked against representative templates and locale variants.

    Cadence: Parity confirmed

  • Markup health scorecard

    Markup validity, visible-fact parity, duplicate entity rate, eligible page coverage and production regression rate, each reported against the pages it applies to. Every metric fixes its scope, the pages counted, the exclusions and the period watched before any comparison runs. A fresh scorecard lands at every affected release, the monthly review covers unresolved variance, and the quarterly one refreshes samples and owners.

    Accepted when

    Markup validity, visible-fact parity, duplicate entity rate, eligible page coverage and production regression rate, each reported against the pages it applies to.

    Cadence: Scope fixed upfront

Before the graph changes

Start with the page, its owner and the release path

Content, technical SEO and engineering owners first settle the page facts, schema boundary and evidence required to approve a release.

A good fit when

  • Markup is missing, invalid or duplicated — Use this when markup is missing, invalid, duplicated across templates or inconsistent with the content a user can see.
  • The team needs an approved schema boundary — Nobody has settled which schemas and relationships fit the page, what stays out of markup, or how parity is monitored.
  • Templates disagree on ownership and parity — Content and markup exist, but ownership, eligibility and visible parity vary by template or environment.
  • Deployment inputs are ready — Bring the page/template inventory, approved facts and entities, JSON-LD results, plus CMS, render and deployment constraints.
  • Page purpose and eligible schema types — Each page template is matched to its actual purpose and to schema types supported by visible, policy-eligible facts.
  • Visible facts, entities and relationships — The graph includes only visitor-verifiable entities and properties, with explicit identifiers and relationship direction.
  • Template ownership and duplication — Before markup changes, we find every CMS, component and plugin layer that emits, merges or duplicates the JSON-LD.
  • Production validation and parity monitoring — Approval uses rendered production JSON-LD, visible parity and regression behavior. Source code alone isn't enough.

Better handled as other work when

  • Markup is expected to force rich results — Validation makes markup accurate and machine-readable. An external system still decides whether to render it.
  • External results are not promised — Valid structured data helps parsers, but cannot guarantee rich results, answer selection, ranking or citation.
  • Validity cannot replace page truth — Valid markup still fails if it overstates the visible offer. Proxies stay labeled and compared with direct observations.

People who watch how AI cites a brand

GEO work starts with recording what AI answers actually say about a brand today, then moves to the parts you can influence. The consultants below work on the specific capability this page covers.

Content we've produced on this topic

Articleby Elif Naz Akan KarakoçStructured Data for AI: Tips for Supporting AI Visibility with SchemaArticleby Samet ÖzsüleymanMost Common JSON-LD Schema Issues and Solutions

Tools we use

Tools behind this work

  • Schema App

    the recurring check that JSON-LD in production still matches the approved graph

  • Google Rich Results Test

    checks eligibility for a rich result, a separate claim from simply valid markup

  • Schema.org Validator

    checks pure syntax and vocabulary conformance, independent of any one engine's rules

  • Screaming Frog

    traces a markup mismatch across the whole site back to the exact template producing it

If the page does not say it, the markup cannot

Check a live template before the next release

Share one representative URL and its approved page facts. The review returns the rendered JSON-LD differences, unsupported properties and production check needed for acceptance.
Validate rendered structured data

Questions we hear

No. Valid markup makes a page's meaning legible to parsers, while rich results, rankings and citations remain separate outcomes measured on their own.