SEO Audit 2026: Add Entity and AEO Checks to Your Technical Review

Fix low traffic fast: add entity and AEO checks to your 2026 technical SEO audit

If your organic traffic stalled in 2025 or conversions aren’t moving despite “on-page” fixes, the most likely gap is that your audit still treats search as only blue links. Since late 2024–2025 the major engines and AI answer platforms shifted to entity-backed answers and answer engines that prioritize concise, verifiable responses. In 2026 a modern technical SEO audit must include entity-based SEO and Answer Engine Optimization (AEO) checks. This article gives you a practical, prioritized checklist and playbook to test and fix the gaps that matter for AI-driven results.

What changed — quick context for busy teams (2024–2026)

Search is now two-layered: traditional retrieval (links and documents) and AI answer surfaces that synthesize content against structured knowledge. Key developments to know:

• Major search providers integrated generative AI answers into SERPs and API products (notably late 2024–2025 updates to Google’s AI features and Microsoft/Bing’s answer experience). Those answers increasingly rely on entity graphs and structured provenance.
• Schema.org and structured-data best practices evolved to carry provenance, claim context, and Q&A structures that feed answer engines (new properties and recommended patterns appeared across 2025).
• Enterprise and consumer AIs now use embeddings, knowledge graphs (Wikidata/knowledge panels), and selective content citation. That raises the bar for authoritative, machine-readable entity signals from sites.

What to add to your SEO audit framework right now

Replace the single “technical” column with three core streams: Technical Indexing, Entity Signals, and AEO Readiness. Run these checks in parallel — many fixes overlap and compound.

Audit stream 1 — Technical Indexing (baseline)

Continue standard technical checks but prioritize fixes that affect machine-readability and structured access:

• Index coverage: Google Search Console & Bing Webmaster index status. Flag blocked canonical pages, paginated content, and thin canonical copies.
• Crawl budget and response codes: fix 4xx/5xx, redirects, and large redirect chains.
• Robots and meta: ensure robots.txt and meta robots don’t block JSON-LD endpoints, /.well-known, or API routes that expose structured data.
• Site speed and Core Web Vitals: practical threshold — LCP <2.5s and CLS <0.1 for answer-critical pages (answers are sensitive to perceived speed).
• Sitemaps: include machine-consumable sitemaps (index and structured sitemaps) and surface a schema-aware sitemap where relevant.

Audit stream 2 — Entity-based SEO checks

Entity signals tell AI which real-world concepts your content represents. These checks ensure your site’s entities are discoverable, canonical, and linked to global graphs.

1. Canonical entity identification

Every primary page should declare what entity it represents and how it relates to other entities.

• Implement an @id pattern in JSON-LD for key entities (products, people, organizations, events, locations). Use a canonical URI such as /entity/{slug}.
• Use schema types appropriate to the domain (Product, Person, Organization, MedicalCondition, Recipe, LocalBusiness, etc.).

2. SameAs and external identifiers

Link your canonical entity to external authority records:

• Add sameAs links to authoritative pages: Wikidata, official social profiles, company registrations, ISBNs, or clinical registries where applicable.
• Where possible include stable IDs (Wikidata QIDs, ISNI, ORCID) in JSON-LD to reduce entity ambiguity.

3. Entity attribute completeness

Answer engines prefer entities with rich, structured attributes:

• Ensure key properties exist (name, description, image, datePublished, taxonomy tags, product attributes, prices, availability, ratings where relevant).
• For people/experts include credentials, affiliation, sameAs, and authored content links.

4. Internal entity graph and content mapping

Create a content-entity mapping spreadsheet that ties URLs to entity IDs and relationships:

• Columns: URL, primary entity ID, entity type, canonical ID, linked entities, last updated, priority.
• Use this map for content pruning, consolidation, and canonicalization.

Audit stream 3 — AEO (Answer Engine Optimization) checks

AEO checks evaluate whether a page can be used as a concise, verifiable source for AI-generated answers.

1. Answer readiness score

Create an answer readiness score (0–100) for priority pages based on:

1. Directness of answer: does the page contain a concise, explicit answer?
2. Structured answer markup (FAQPage, QAPage, HowTo, or custom Answer properties).
3. Provenance: author, date, citations to primary sources.
4. Entity clarity: page maps to a single canonical entity ID.
5. Freshness and update cadence.

2. Explicit Q&A and summary sections

Answer engines prioritize clear TL;DRs and explicit Q&A blocks. Add machine-friendly elements:

• Short, 1–3 sentence synopsis at the top with structured data (use an abstract or description property in JSON-LD).
• FAQPage or QAPage JSON-LD for commonly asked queries. Add canonical answers, not simply links to other pages.

3. Citation and provenance markup

Engines want to show where claims come from. Schema has matured to support this — and answer AIs use it.

• Include author and organization schema on claims and research pages. Add citation lists in article metadata where appropriate.
• For data-driven claims, link to source datasets and use sameAs or direct dataset URLs in JSON-LD.

4. Conciseness + supporting context

AI answers often present a short answer plus a “why” or steps. Structure pages so the short answer and the supporting detail are clearly separable (use headings, summary boxes, and structured data).

How to test these signals — practical steps

Run the following automated and manual tests. Put results into a prioritization matrix (Impact vs Effort).

Automated checks

• Screaming Frog / Sitebulb / Sitebulb crawl for JSON-LD presence and errors. Extract all JSON-LD and check for schema types, @id, sameAs fields.
• Custom script: extract entity IDs from JSON-LD and validate against the Wikidata API to detect mismatches or missing external IDs.
• Use Google Rich Results test and Schema.org validator to catch syntax and property errors.
• Run an embedding similarity check for canonical pages: generate embeddings for high-value queries and compare to page content embeddings (OpenAI, Vertex AI, or self-hosted). Low similarity suggests content doesn’t answer the query well.

Manual checks

• Simulate queries in live answer platforms (Google SGE, Bing Chat, Perplexity). Note whether your site is used as provenance and whether the answer is accurate.
• Open key pages and verify a clear top-line answer exists and is marked up with JSON-LD.
• Check entity maps: are duplicate pages representing the same entity? Consolidate or canonicalize.

Prioritization framework — what to fix first

Use a simple Impact x Effort scoring. Attribute impact to business value (revenue, conversions, strategic keywords) and expected SERP/AEO improvements.

1. Quick wins (High impact, Low effort): Add TL;DR summary, FAQPage JSON-LD for high-impression pages, sameAs links to high-authority identifiers, fix simple JSON-LD syntax errors.
2. Medium (High impact, Medium effort): Implement canonical @id for product and service pages, embed author and provenance metadata, add structured data for pricing and availability.
3. Strategic (High impact, High effort): Rebuild content to map to a clean entity model (content consolidation, canonical redirects to an entity hub), implement an internal knowledge graph or vector DB, add automated JSON-LD generation to CMS.

Example fixes and JSON-LD patterns

Two examples you can deploy this week.

1. TL;DR summary + FAQ

{
  "@context": "https://schema.org",
  "@type": "Article",
  "@id": "https://example.com/entity/solar-loan-123",
  "name": "Solar Loan Rates — Quick Answer",
  "description": "Current solar loan rates for homeowners: typical APR range and eligibility summary.",
  "author": {"@type": "Person","name": "Jane Doe","sameAs": "https://orcid.org/0000-0002-xyz"},
  "mainEntity": {
    "@type": "Question",
    "name": "What are solar loan rates?",
    "acceptedAnswer": {"@type": "Answer","text": "Typical APRs range between 4.5%–7.0% for qualified borrowers (2026). See details below."}
  }
}

2. Product / entity canonicalization

{
  "@context": "https://schema.org",
  "@type": "Product",
  "@id": "https://example.com/product/sku-xyz#product",
  "name": "Acme Smart Thermostat",
  "sameAs": ["https://www.wikidata.org/wiki/Q123456"],
  "brand": {"@type":"Organization","name":"Acme"},
  "offers": {"@type":"Offer","price":"129.00","priceCurrency":"USD"}
}

Reporting and measuring success

Track both classic SEO KPIs and AEO-specific signals:

• Impressions and clicks from Search Console and Bing (watch for changes after schema updates).
• Answer surface placements and provenance citations observed in SGE/Bing Chat/Perplexity tests.
• CTR and dwell time changes for pages with added TL;DR and FAQ markup.
• Entity knowledge panel changes: new links to site or updated panel data can signal successful entity signals.
• Embedding similarity improvements when you re-run embedding tests for target queries.

Common pitfalls and how to avoid them

• Over-markup: don’t add FAQ markup to pages with thin or duplicate answers — quality matters. Search platforms penalize misleading markup.
• Ambiguous entities: avoid multiple pages claiming the same entity without canonicalization. Use redirects, rel=canonical, and @id to assert ownership.
• Missing provenance: if you provide data or claims, back them with citations. Lack of credible sources reduces the chance an AI will cite your page.
• Complex JavaScript-only JSON-LD: generate server-side JSON-LD where possible to ensure consistent crawlability by bots and answer engines. See our case study for a migration pattern that moved JSON-LD generation server-side during a replatform.

“In 2026, the sites that win AI answers are the ones that make entities and evidence easy for machines to read.”

Team and process changes — make this repeatable

Turn this audit into a scalable process:

• Integrate entity mapping into content workflows: new content must declare primary entity and provide sameAs links.
• Automate JSON-LD validation in CI/CD and CMS publishing flows (Kubernetes and runtime patterns are useful here).
• Schedule quarterly AEO tests that run simulated queries across top answer engines and log provenance usage.
• Train content and product teams on writing short, factual summaries and adding quality citations.

Tools and APIs to include in your 2026 audit toolkit

• Google Search Console & Rich Results Test
• Screaming Frog / Sitebulb (JSON-LD extraction)
• Wikidata API & SPARQL endpoint for entity verification
• Embedding APIs (OpenAI, Vertex AI) for content-query similarity tests
• Perplexity, Bing Chat, and Google SGE for answer provenance checks
• Custom scripts to validate @id and sameAs consistency across the site

Wrap-up: Your next 90-day plan

1. Week 1–2: Run automated JSON-LD extraction and create the content-entity map for your top 200 pages.
2. Week 3–6: Deploy quick wins — TL;DR summary, FAQ JSON-LD on top pages, fix JSON-LD syntax errors.
3. Month 2: Implement entity canonicalization (@id, sameAs) and server-side JSON-LD generation for product and author pages (see the migration case study for patterns).
4. Month 3: Run AEO simulation tests (SGE/Bing/Perplexity), measure provenance usage, and prioritize strategic rebuilds for high-impact entities.

Final takeaways

In 2026, a technical SEO audit that ignores entity signals and AEO readiness is incomplete. Focus on machine-readable entity IDs, credible provenance, concise answers, and repeatable processes. Prioritize fixes by impact and effort — quick structured-data wins often unlock more visibility in AI answers than incremental content tweaks alone.

Ready to update your audit to 2026 standards? Export your content-entity map, run a JSON-LD extraction, and test five priority queries in SGE/Bing today. If you want a plug-and-play checklist and a prioritized action plan tailored to your site, request our 90-day AEO + Entity Audit template.

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