AI Visibility Foundation | Entity & Schema Audit

AI Engines Are Already Deciding Whether Your Brand Is Worth Mentioning

Every time someone asks ChatGPT, Perplexity, Gemini, or another AI engine about businesses in your category, those systems are making confidence assessments about which brands to include. That confidence is built from structured entity data — your presence in Wikipedia, Wikidata, Google's Knowledge Graph, authoritative directories, and news archives — and from schema markup on your own website. If either layer is thin, inconsistent, or invalid, the AI engine's confidence in your brand drops, and so does your likelihood of being cited.

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This is for you if

WHO THIS IS FOR

Businesses that have never audited their entity data completeness. Knowing that your Google Business Profile is claimed and your LinkedIn company page is active does not mean your entity data is complete. Most US businesses have significant gaps in their structured entity coverage — absent or thin Wikidata entries, missing attributes in Google's Knowledge Graph, inconsistent data across industry directories, news archives that reference outdated company information — and they have never had a systematic audit that surfaces all of it in one place. If you have never run an entity audit, you are almost certainly operating with gaps you do not know about.

Companies preparing to invest in AI visibility programmes. US businesses are accelerating investment in GEO, AI answer optimisation, and brand mention monitoring as AI-generated responses displace traditional search traffic. Those investments perform significantly better when they are built on a verified entity foundation. A GEO content programme that targets AI citation in category queries will produce better results when the entity layer is clean, consistent, and complete. This audit is the diagnostic that establishes that foundation.

Businesses with schema errors flagged in Google Search Console. GSC's Enhancement reports flag Organisation, Product, Article, FAQ, and other schema errors for US websites regularly. Businesses in e-commerce, SaaS, professional services, healthcare, and media carry schema errors — introduced by outdated plugins, theme defaults, or one-off developer implementations — that have never been corrected. Those errors actively suppress rich result eligibility and reduce AI engine confidence in your page content. This audit identifies every error, classifies it by severity, and produces a repair plan.

Mid-market and enterprise brands managing complex entity footprints. Larger US businesses often have multiple product lines, regional offices, subsidiary brands, and years of M&A history, all of which create complex entity situations: multiple Knowledge Graph entries that should be unified, contradictory founding dates from acquisition news coverage, outdated industry classifications from legacy directory submissions. This audit maps all of it and produces a rationalisation plan so your entity data tells a single, accurate story.

What's broken

WHAT'S BROKEN

AI engines are not confident enough in your entity to cite you consistently.

AI engine citation is, in part, a confidence function. When ChatGPT or Perplexity processes a query about businesses in your category, it weights entities it can verify with multiple consistent, authoritative sources higher than entities with thin or contradictory data. Most US businesses believe their entity presence is adequate because they appear in some Google searches and have a Wikipedia article. In reality, the quality and completeness of their entity data — across Wikidata, structured directories, news archives, and other sources — is frequently insufficient to generate consistent AI citation at the level their brand reputation would suggest.

Entity data contradicts itself across authoritative sources.

US businesses accumulate contradictions across entity sources over time without realising it. A company that rebranded three years ago may still have its old name in Crunchbase, a founding date discrepancy between its Wikipedia article and its EDGAR filings, a different industry classification in Wikidata than it uses on its website, and outdated revenue data in news archives. Each individual contradiction seems minor. Collectively, they reduce the coherence of your entity picture in AI training data — and AI engines trained on contradictory data about your brand will produce inconsistent or inaccurate responses about you.

Schema markup is a maintenance problem that never gets addressed.

The typical US business website has schema markup that was implemented during a site build or SEO project and has never been revisited. Schema.org evolves, Google's requirements change, and websites change — new pages get added without schema, old schema references URLs that no longer exist, plugin updates quietly break previously valid markup. The result is a schema layer that is partially functional at best, actively generating errors at worst, and never validated against current standards.

There is no single view of entity health, so no one is accountable for it.

SEO teams track keyword rankings. Content teams track traffic. PR teams track media coverage. But nobody owns the entity layer — nobody is monitoring whether Wikidata attributes are accurate, whether Knowledge Graph data is current, whether schema across the full site is valid. Without ownership and a baseline measurement, entity health degrades silently, and the consequences show up as AI citation gaps that are hard to diagnose after the fact.

What we engineer

WHAT WE DO

25-source entity coverage map

A complete inventory of your brand's presence or absence across Wikipedia, Wikidata, Google Knowledge Graph, Google Business Profile, Crunchbase, LinkedIn Company Pages, Bloomberg company profiles, SEC/EDGAR for public companies, Hoovers, D&B, industry-specific US directories and trade association member lists, regional business journal coverage, national news archives, Apple Maps, Bing Places, Yelp, and additional sources relevant to your category and market segment. Each source is scored for presence, completeness, and accuracy.

Schema crawl report

A full extraction of every schema markup instance across your website, covering Organisation, LocalBusiness, Product, Article, FAQ, HowTo, BreadcrumbList, SoftwareApplication, Event, and any other types present. Every URL with schema is documented with its markup type and content.

Validation report

Every schema instance validated against current Schema.org standards and Google's structured data requirements, with each error categorised as critical, warning, or advisory.

Error severity ranking

Schema errors ranked by their impact on AI engine comprehension and rich result eligibility, so your development team can triage immediately.

Entity gap analysis

A source-by-source breakdown of missing data, inaccurate entries, and contradictions between sources, with specific correction instructions for each identified issue.

Priority fix roadmap

A scored, sequenced action list combining entity fixes and schema corrections, ordered by expected AI visibility impact, with effort estimates for planning purposes.

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What changes

WHAT CHANGES

Before

After

Before You have a verified, scored baseline for AI engine comprehension of your brand.

After Before this audit, your entity health was largely unknown — you had working assumptions but no verified data. After it, you have a source-by-source coverage map, a schema error inventory, and a single entity health score you can track over time. That baseline is the starting point for a measurable AI visibility programme.

Before Your teams have clear, specific tasks rather than open-ended directives.

After The fix roadmap is not a recommendation document. It is a ranked to-do list with specific instructions: which directory listings to correct, which Wikidata attributes to add, which schema errors to fix in the next development sprint, which Knowledge Graph submissions to make. Your SEO team, PR team, and development team each have a concrete set of actions with sequencing and effort estimates.

Before AI engines have cleaner, more consistent data to work from.

After Closing the highest-priority entity gaps and correcting the critical schema errors means AI engines training on or indexing your brand have a more coherent, more complete picture to work with. The practical outcome is more consistent and more accurate brand citation in AI-generated responses — your brand appears where it should, attributed correctly.

Before Your AI visibility investment produces compounding returns.

After A verified entity foundation makes every subsequent AI visibility action more effective. GEO content that reinforces accurate entity attributes produces stronger signals than content built on a fragmented foundation. PR coverage that adds consistent data to authoritative sources compounds over time. This audit does not just address today's problems — it improves the performance of everything that follows.

Common questions

FAQ

What is an entity in the context of AI engine performance?

An entity is a uniquely identified concept — your business — that AI engines use as a structured reference point rather than treating your brand name as an unresolved text string. Google's Knowledge Graph, Wikidata, and similar knowledge bases store structured facts about entities: official name, industry classification, founding date, headquarters location, key people, related organisations, and more. When AI engines process queries about businesses in your category, they resolve brand references to known entities and draw on all associated structured data to form their responses. A business with a strong, consistent, and complete entity record is cited more confidently and more frequently than one with thin or contradictory entity data.

What is schema markup and why does it matter beyond SEO?

Schema markup is structured data — typically JSON-LD — embedded in your website's HTML that describes each page's contents using a standardised vocabulary from Schema.org. It tells AI crawlers and search engines explicitly whether a page contains a product, a service, an article, a FAQ, a business, or other content types. While schema markup is well known for its role in rich results in traditional search, its relevance to AI engine performance is equally significant: AI engines use schema markup alongside entity data to form a more accurate and complete understanding of your website. Invalid or missing schema forces AI engines to infer page meaning from unstructured text, which is both less accurate and less comprehensive.

What 25 sources does the entity coverage map check?

The 25 sources include Wikipedia, Wikidata, Google Knowledge Graph, Google Business Profile, Crunchbase, LinkedIn Company Pages, Bloomberg company profiles, SEC EDGAR (for applicable companies), D&B and Hoovers business registries, industry association member directories specific to your sector, US national and regional news archives, Apple Maps, Bing Places, Yelp, Facebook Business Pages, YouTube channel data for media brands, review platforms relevant to your industry, and additional structured databases AI engines are known to use for entity resolution. The exact list is adapted to your specific business category, size, and entity complexity.

How long does the audit take?

The Entity & Schema Audit is completed within 7 to 10 business days from receipt of your brand information brief. Delivery includes the full 25-source coverage map, schema crawl report, validation report, error severity ranking, entity gap analysis, and priority fix roadmap as a single packaged deliverable. For large enterprise websites with multiple subdomains or businesses with complex multi-entity structures, we advise on any timeline adjustments before starting.

What does the audit cost?

The Entity & Schema Audit is priced from USD 1,100 for a standard US business. Final pricing depends on website size, entity footprint complexity, and whether multi-brand or multi-entity coverage is required. We provide a fixed-fee quote after a brief scoping call. There are no ongoing fees — this is a one-time diagnostic engagement with a clean, fixed scope.

What happens after the audit is delivered?

After delivery you have three paths. You can implement the priority fix roadmap using your own SEO, PR, and development teams, working from the detailed correction instructions in the report. You can engage Ignited Nepal to implement the fixes as a separate project, executing the roadmap in priority order. Or you can use the audit as the entry point for a broader AI visibility programme — feeding the findings into a GEO content strategy, entity authority building, or structured brand answer accuracy work. The audit is designed to be immediately actionable as a standalone deliverable, and it integrates directly with every other AI visibility service Ignited Nepal provides.

Our team

The people behind the work

Not a black box. Real specialists you can call, with their names on the work.

Niraj Raut

Founder — Ecommerce SEO

Keshab Joshi

PPC Expert

Hawrry Bhattarai

Google Ads Expert

Arogya Rijal

SaaS SEO Expert

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Your competitors are being cited by AI engines. Find out whether you are, and why.

AI citation gaps are not obvious until you measure them. The Entity & Schema Audit gives you the complete, source-by-source picture of how AI engines currently understand your brand, what structured data is missing or wrong, and what to fix first to close the gap — with no guesswork and no vague recommendations.

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