Nadia Mohamed
A structural audit of a multilingual learning platform across eleven locales
A three-week structural audit — and a single structured-data gap that became the spine of the action plan.
The challenge
The client runs a headless, multilingual stack serving learners across eleven language markets — a meaningful engineering surface, and a hard one to audit. The team had Ahrefs, Search Console and GA4 in-house; what they didn't have was a structural read on the site itself.
Was Googlebot actually receiving what they thought it was? Was the hreflang implementation defensible at scale across eleven locales? Was the structured-data layer doing any real work? They needed an outside answer to the architecture, not a list of generic SEO tips.
The approach
A fixed three-week engagement, sequenced deliberately. Week one: lock down access and audit the technical foundations — crawl, rendering, hreflang, sitemap, robots. Week two: the architectural and entity layers — URL logic, canonical strategy, structured-data strategy, brand-entity audit. Week three: synthesise and ship.
No keyword research, no content briefs, no implementation — strictly the architecture. Every finding traceable to a verified source, every recommendation pre-rated for severity, owner, and effort.
Two core outputs designed to be read together: a narrative document that explains the reasoning, and a workbook engineering executes from.
What the audit found
The technical foundation was sound — and that mattered. Server-side rendering was clean across every template. Hreflang reciprocity was perfect within declared pairs. Internal link integrity was excellent. The canonical layer was unusually clean — the cross-locale canonical bug that tends to bite multilingual headless stacks simply wasn't present.
The headline finding
The site emits no structured data. Anywhere.
None — across every page template, every locale, every URL. That single gap leaves the client ineligible for rich results (video, breadcrumbs, app ratings, article enhancements) and, with AI Overviews now an increasingly visible feature of search in their space, harder for AI search to read accurately.
The structured-data rollout became the spine of the action plan, with most of the other work sequencing around or behind it.
Beyond that, a cluster of smaller, more contained findings — a content-authoring error in a localised slug, a homepage layout-shift that traced to one shifting element, a stale page title left over from the previous year, a brand-entity gap visible in the Wikidata entry. Each one logged, each one rated, each one with a fix the right person could action.
What was delivered
Six pieces, designed for an async client team across engineering, content and brand:
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Master Audit Document
The long-form narrative. Eleven sections, every finding written up with what was found, why it matters, the impact, the fix, and a severity rating.
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Dev-Ready Action Plan
The execution workbook. Every finding as one row — severity, owner (frontend / backend / CMS / content), estimated effort, expected impact, and a cross-reference back to the document. Plus six per-workstream detail tabs: rendering, hreflang, canonical, URL architecture, structured data, sitemap & robots.
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Schema templates
Ten ready-to-implement JSON-LD blocks, pre-filled with the client's content structure. Patterns Google has discontinued (HowTo, the sitelinks search box, the legacy video-carousel markup) explicitly excluded.
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Schema Implementation Guide
Rollout order, locale handling, and the pitfalls to avoid.
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Video Schema Reference Guide
The decision tree for when a video earns its own markup and when to nest it inside an Article.
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Baseline Data Overview
Verified pre-audit numbers so the team can measure progress from here against a known starting point.
Plus a short walkthrough video, recorded async so anyone the team forwards it to can watch in their own time.
What this unlocks
The team got what they came for: a structural answer they can act on, in a workbook their developers can open and start sequencing immediately. The structured-data rollout is laid out as concrete batches of work — sitewide foundation first, then the high-traffic templates, then the specialised layers — rather than as a wishlist.
And the headline finding is sharp enough to defend internally: the single biggest opportunity in the audit is also the one with the lowest implementation risk and the clearest benefit across teams.
The audit was delivered on time, on scope, and within the fixed fee.
A similar surface?
If you're sitting on a multilingual platform where the basics seem to work but the structural picture is unclear, the Technical SEO Architecture Audit is built for exactly this: fixed-scope, fixed-fee, three weeks, ending with a workbook your developers can execute from.