A clear scope
What SCHEMA means here
Structured data is machine-readable information describing the content of a page. For ecommerce, relevant markup can describe products, offers and supported variant relationships. Correct markup supports understanding and eligibility; it does not guarantee a rich result.
This service owns structured-data mapping, implementation and validation. Feed diagnostics and broader crawl architecture are related work. We do not invent ratings, reviews, certifications or product facts to fill a schema field.
The buyer problem
Where the work makes a difference
Plugins and themes can emit conflicting product records, stale availability or review information that is not substantiated. A template may pass a syntax check while describing an offer that customers cannot buy.
We trace each supported property to its actual data source and compare the rendered markup with the page and checkout context. Work focuses on applicable types and accurate fields, not adding every possible property. Representative products and variants are tested before the implementation is extended across templates.
BrownBag’s approach
From evidence to useful improvements
Map entities and source fields
Review the platform, existing markup emitters and catalogue model. Identify supported product and variant cases, source fields and missing information, including ownership of price, stock and other changing attributes.
Implement and test representative records
Prepare the agreed template or integration changes and remove conflicting outputs where needed. Validate examples with relevant tools and compare each record with what a shopper can see and purchase.
Monitor changes and maintain coverage
Check released output and available enhancement reports. Document how catalogue, theme and plugin changes affect the markup, then establish regression cases for out-of-stock products, variants and other important states.
Your handover
What you receive
- A structured-data inventory and source-field mapping
- Scoped template changes or developer implementation specifications
- Validation evidence for representative product states
- A maintenance checklist and regression sample for future releases
Evidence, with context
How we measure progress
- Track valid applicable records and unresolved errors for the agreed product sample.
- Review search appearance reports where available without treating eligibility as guaranteed display.
- Monitor mismatches between markup, visible product facts and changing catalogue data.
Scope your next step
Bring us the problem you want to solve.
These details help us agree the deliverables, responsibilities and review points:
- Store platform and access to representative product templates
- Reliable product, offer and variant source data
- Existing markup tools and the contact responsible for catalogue updates
Start with a focused scope. We confirm access, implementation ownership and any platform eligibility before the work begins.
Your questions, answered
Is a successful validator result enough?
No. The markup also needs to follow the applicable rules and match real visible information. We check both syntax and the underlying facts.
Can schema guarantee AI citations?
No. Structured data describes content; it does not control source selection in AI answers. We use supported markup for a clear purpose.
Can you fix duplicated plugin markup?
Yes, where access and scope permit. We first identify which component owns each output, then agree a change that preserves accurate data and avoids competing records.