AI Visibility Foundation | Entity & Schema Audit

If AI Engines Can't Verify Your Brand, They Won't Recommend It

When a potential customer in Sydney, Melbourne, or Brisbane asks ChatGPT or Perplexity which businesses to consider in your category, the AI engine does not run a live search. It draws on structured entity data it has already processed — from Wikipedia, Wikidata, Google's Knowledge Graph, Australian business directories, and dozens of other structured sources — combined with schema markup it has read from your website. If that data is incomplete, inconsistent, or incorrect, the AI engine either omits your brand or misrepresents it. The Entity & Schema Audit from Ignited Nepal is a comprehensive diagnostic that maps your brand's structured data presence across 25 sources — including Australian-specific directories and registries — crawls and validates every schema instance on your website, and produces a prioritised fix list. It is the one piece of work that tells you precisely what the AI engine layer sees when it looks for your brand, and what to fix first.

Run an AI Visibility Audit → View the evidence →
This is for you if

Who This Is For

Businesses that have never verified their entity data completeness. Operating an ABN-registered business in Australia does not automatically create a correct, complete entity record across the sources AI engines consult. Most Australian businesses have never checked whether their brand is correctly represented in Google's Knowledge Graph, whether their Wikidata entry is accurate or even exists, or whether Australian directories like ABN Lookup, the ASIC company register, Yellow Pages Australia, and True Local are carrying current and consistent information. The gap between what you think your structured data says and what AI engines actually read is typically significant — and the only way to know is to audit it.

Companies preparing to invest in AI visibility programmes. Australian businesses are increasingly aware that GEO (Generative Engine Optimisation) is becoming as important as traditional SEO, and many are beginning to allocate budget toward it. But a GEO content programme, a brand mention monitoring strategy, or a structured PR campaign built on top of a broken or incomplete entity foundation will underperform. This audit gives you the verified baseline your programme needs — so you are fixing the right things before spending on content, link building, or outreach.

Businesses that have seen structured data errors in Google Search Console. GSC's Enhancement reports now flag Organisation, Product, FAQ, and other schema errors directly. Australian businesses in e-commerce, professional services, healthcare, and hospitality routinely carry schema errors that were introduced years ago and have never been corrected. Those errors are actively suppressing rich result eligibility and sending poor signals to AI engines. This audit diagnoses every error, ranks them by severity, and produces a repair plan.

Established Australian brands entering new markets or scaling nationally. When a business grows from a regional presence to a national one, or begins targeting international audiences, AI engines in those new markets need to recognise the brand as a documented, credible entity. A thin or geographically incomplete entity footprint limits how AI engines present the brand outside its original market. This audit maps those gaps before you invest in expansion.

What's broken

What's Broken

AI engines cannot place your brand with confidence.

AI engines like ChatGPT, Gemini, and Perplexity resolve brand queries by matching them to known entities in their training data. If your business is absent from Google's Knowledge Graph, if your Wikipedia entry is a stub or does not exist, if your Wikidata record is missing key attributes, the AI engine cannot confidently match a query about your category to your brand. You may be the most capable business in your space, but if the structured entity layer does not know you exist — or knows you incompletely — you will be underrepresented or absent in AI-generated responses.

Australian directory data is inconsistent and outdated.

Yellow Pages Australia, True Local, Yelp AU, LinkedIn, Hotfrog, and other directories that AI engines index for entity signals are frequently carrying outdated ABNs, old addresses, incorrect trading names, or category misclassifications. Every inconsistency across those sources reduces AI engine confidence in your entity. The problem compounds over time: a business that moved offices three years ago and updated its GBP but not its directory listings is carrying contradictory location data across dozens of sources — and AI engines weight that inconsistency negatively.

Schema markup was added once and never checked again.

Most Australian businesses that have schema markup added it through a WordPress plugin, a Shopify theme, or a one-time developer implementation. That markup has typically never been retested since the original implementation. Schema.org standards evolve, Google's requirements change, and markup that was valid two years ago may now carry errors or miss required properties. Unmaintained schema is a quiet liability that accumulates without triggering obvious symptoms.

There is no consolidated view of entity health.

Marketing managers know the GBP is claimed and verified. The developer knows schema is present. The business owner assumes Australian directory listings are correct because they were set up years ago. But nobody has compiled all of that into a single health picture, scored each source, and identified which gaps are actually affecting AI engine performance. Without that picture, there is no basis for prioritising fixes, no baseline for measuring improvement, and no way to demonstrate ROI on AI visibility work.

What we engineer

What We Do

25-source entity coverage map

A complete inventory of your brand's presence or absence across Wikipedia (English and Australian content), Wikidata, Google Knowledge Graph, Google Business Profile, ABN Lookup, ASIC company register, Crunchbase, LinkedIn Company Pages, Yellow Pages Australia, True Local, Hotfrog, industry-specific Australian directories, news archives including Australian media, Apple Maps, Bing Places, and additional sources relevant to your category and geography. 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, and any other schema 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

A ranked list of schema errors ordered by their impact on AI engine comprehension and rich result eligibility.

Entity gap analysis

A source-by-source breakdown of missing data, inaccurate entries, and contradictions, 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 each item.

Run an AI Visibility Audit →
What changes

What Changes

Before
After
Before Before this audit, your understanding of your structured data presence was probably based on assumptions and partial information.
After You have a verified, source-by-source picture of your entity health. After it, you have a scored coverage map across all 25 sources — including the Australian-specific ones that matter most for local and national AI engine performance — and you know exactly where the gaps and errors are.
Before Your team has a vague strategy with general recommendations.
After Your team has a concrete to-do list, not a vague strategy. This audit produces a ranked, sequenced fix list with specific instructions for each action. Your marketing manager knows which directories to update. Your developer knows which schema errors to correct this sprint. Your SEO partner knows which entity gaps to prioritise in the next outreach cycle. Everyone is working from the same prioritised plan.
Before The structured entity layer knows your brand incompletely or inconsistently.
After Your brand becomes more citable by AI engines. When the highest-priority entity gaps are closed and the critical schema errors are resolved, AI engines have a more complete, more consistent, and more accurate structured data picture of your business. The practical result is that your brand becomes a more confident match for relevant queries — AI engines are more likely to cite you, mention you accurately, and include you in category-level responses.
Before AI visibility investments are built on fragmented data.
After Every subsequent AI visibility investment performs better. A GEO content programme layered on top of a verified entity foundation produces better results than one built on fragmented data. PR coverage that reinforces accurate entity attributes produces stronger signals than coverage that contradicts existing records. This audit does not just fix problems — it makes the work that follows it more effective.
Common questions

FAQ

What is an entity in the context of AI visibility?

An entity is a uniquely identified concept — in this case, your business — that AI engines use as a structured reference point rather than treating your brand name as a plain text string. Google's Knowledge Graph, Wikidata, and similar databases store structured facts about entities: name, industry, founding date, location, related organisations, and more. When AI engines process queries, they resolve brand references to known entities and draw on all associated structured data. A business that is not established as a recognised entity, or whose entity data is incomplete, is at a systematic disadvantage when AI engines are deciding which brands to include in their responses.

What is schema markup and why does it matter for AI?

Schema markup is structured data — typically written as JSON-LD — embedded in your website's HTML that describes the contents of each page using a standardised vocabulary from Schema.org. It tells AI crawlers explicitly whether a page contains a product, a service, an article, a FAQ, a business listing, or other content type. AI engines use schema markup alongside entity data to form a more accurate understanding of your website's content. Invalid, incomplete, or missing schema means AI engines must infer meaning from unstructured text, which is less reliable and less comprehensive than reading correctly implemented structured data.

What are the 25 sources covered in the entity audit?

The 25 sources include Wikipedia (English edition and relevant Australian content), Wikidata, Google Knowledge Graph, Google Business Profile, ABN Lookup, ASIC company register, Crunchbase, LinkedIn Company Pages, Yellow Pages Australia, True Local, Hotfrog, Yelp AU, Apple Maps, Bing Places, Facebook Business Pages, industry association member directories, Australian news archives, government tender registers where relevant, review platforms specific 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 and geographic footprint.

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 coverage map, schema crawl, validation report, and priority fix roadmap as a single packaged deliverable. For large e-commerce websites or businesses with unusually complex multi-brand entity structures, we advise on any timeline adjustment before starting.

What does the audit cost?

The Entity & Schema Audit is priced from AUD 1,200 for a standard Australian business. Final pricing depends on website size and entity footprint complexity. We provide a fixed-fee quote after a brief scoping call. There are no ongoing fees attached to this engagement — it is a one-time diagnostic.

What happens after the audit?

After delivery you can implement the fix roadmap using your own team, using the detailed correction instructions included in the report. You can engage Ignited Nepal to implement the fixes as a separate project. Or you can use the audit as the foundation for a broader AI visibility programme — feeding the findings into a GEO content strategy, entity authority building, or brand answer accuracy work. The audit is designed to be actionable as a standalone deliverable, and it also 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

Niraj Raut

Founder — Ecommerce SEO
Keshab Joshi

Keshab Joshi

PPC Expert
Hawrry Bhattarai

Hawrry Bhattarai

Google Ads Expert
Arogya Rijal

Arogya Rijal

SaaS SEO Expert
About Us →
Start here

Find out exactly what AI engines see when they look for your brand.

The gap between your actual brand reputation and your AI-engine-visible entity health can be significant — and it widens quietly every month that structured data problems go unfixed. The Entity & Schema Audit closes that information gap with a complete, source-by-source diagnostic and a prioritised fix list your team can act on immediately.

Run an AI Visibility Audit →

Run an AI Visibility Audit