AI Visibility Foundation | Entity & Schema Audit

Does Any AI Engine Actually Know Who You Are?

AI engines — ChatGPT, Gemini, Perplexity, Google's AI Overviews — don't browse your website the way a human does. They build their understanding of your brand from structured data: entity records in Wikipedia, Wikidata, Google's Knowledge Graph, and dozens of other sources, combined with schema markup embedded in your pages. If that structured data is incomplete, contradictory, or missing entirely, AI engines either ignore your brand or get it wrong.

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This is for you if

Who this is for

Businesses that have no idea whether their entity data is complete. Most companies in Nepal have never checked whether their brand appears correctly in Google's Knowledge Graph, whether their Wikidata entry exists and is accurate, or whether the 25 sources AI engines consult are telling a consistent story. If you have never audited your structured data presence, you are almost certainly missing entries, carrying outdated information, or presenting contradictory signals across sources — and you would not know unless you looked.

Companies preparing to invest in AI visibility work. If you are about to run a GEO (Generative Engine Optimisation) programme, build topical authority content, or submit brand prompts to AI monitoring tools, you need to know the state of your entity foundation first. Running AI visibility campaigns on top of broken or incomplete entity data is like building on sand. This audit gives you the verified baseline your programme needs before spending on content or outreach.

Businesses that have seen schema errors flagged in Google Search Console or third-party validators. GSC now surfaces structured data errors and warnings directly in its Enhancement reports. If you have received notifications about invalid Organisation markup, broken Product schema, or missing required fields, those errors are suppressing your eligibility for rich results — and they are also sending low-quality signals to AI engines that read the same structured data. This audit diagnoses every error, scores it by severity, and tells you what to fix first.

Established Nepali brands preparing for regional or international expansion. When a business based in Kathmandu begins targeting audiences in India, the Gulf, or further abroad, AI engines in those markets need to recognise the brand as a legitimate, well-documented entity. An entity footprint that is thin or Nepal-only will limit how those AI engines present the brand to international users. This audit maps the gaps before the expansion spend begins.

What's broken

AI engines cannot reliably identify your brand.

When a user in Nepal — or anywhere — asks ChatGPT or Gemini about businesses in your category, those AI engines pull from entity data, not from your website copy. If your brand's entity record is absent from Google's Knowledge Graph, if your Wikipedia or Wikidata entry does not exist or is thin, if your Crunchbase and LinkedIn entries have different founding dates and industry classifications, the AI engine cannot confidently map a query to your business. It will either skip you or attribute incorrect information to you. Neither outcome serves you.

Your entity data contradicts itself across sources.

Over years of operating, most businesses accumulate inconsistent data across directories, press mentions, social profiles, and structured databases. Your registered company name might differ from your trading name, which differs again from your GBP listing. Your founding year might appear as three different values across Wikipedia, Crunchbase, and news archives. Your industry classification might be wrong or absent on Wikidata. Every contradiction is a signal that reduces AI engine confidence in your entity — and low-confidence entities get cited less.

Your schema markup has never been properly validated.

Many Nepali businesses added schema markup to their website years ago, either through a plugin, a developer one-off, or a theme default. That markup has never been tested against current Schema.org standards, never validated against Google's requirements, and never checked for the specific errors — missing `@id` fields, incorrect property types, orphaned nodes — that quietly disqualify pages from rich results and suppress AI engine comprehension of your content.

Nobody has a single view of entity health.

Your marketing team might know your GBP is set up. Your developer might know schema was added to the homepage. But nobody has mapped all 25 relevant sources, scored each one for completeness and accuracy, and produced a unified health score. Without that single view, you cannot prioritise, you cannot track improvement over time, and you cannot demonstrate to leadership that the AI visibility work is moving the right needles.

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, industry-specific directories, news archives, government business registries, and additional sources relevant to your category. 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 custom types present. Every URL with schema is documented.

Validation report

every schema instance validated against 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, so you know which fixes produce the most return.

Entity gap analysis

a source-by-source breakdown of what entity data is missing, what is inaccurate, and what contradicts other sources, with specific correction instructions for each gap.

Priority fix roadmap

a scored, sequenced action list combining entity fixes and schema corrections, ordered by AI visibility impact, so your team or developer can work through them in the right order.

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What changes

Before

After

Before Before this audit, your entity health was largely unknown.

After You have a clear, verified picture of where your brand stands. After it, you have a source-by-source map, a schema error inventory, and a single health score you can track going forward. That visibility alone changes how decisions get made — you stop guessing whether AI visibility work is worth doing and start knowing which specific fixes will move the needle.

Before Generic SEO advice tells you to "improve your schema" or "build entity authority."

After You have a prioritised action list, not a general recommendation. This audit tells you exactly which five entity sources to update first, which three schema errors to fix this week, and what the expected AI visibility impact of each action is. Your team has a concrete to-do list, not a strategy document that sits unread.

Before AI engines work from incomplete and inconsistent data about your business.

After Your AI engine citations become more reliable. Once the highest-priority entity gaps are closed and the critical schema errors are corrected, AI engines have cleaner, more consistent data to work from. That translates into more accurate brand mentions, greater willingness by AI engines to cite your brand in relevant queries, and reduced risk of AI engines presenting incorrect information about your business to potential customers.

Before Subsequent AI visibility investment is built on an unverified foundation.

After Your foundation is in place for everything that comes next. Whether the next step is a GEO content programme, a structured PR push to build topical authority, or a brand answer accuracy campaign, all of it performs better when the entity foundation is sound. The audit does not just fix today's problems — it makes every subsequent AI visibility investment more effective.

Common questions

FAQ

What is an entity, and why does it matter for AI visibility?

An entity is a named, uniquely identifiable thing — in this context, your business — that knowledge databases and AI engines use to represent your brand as a structured concept rather than just a string of text. When AI engines process queries, they resolve brand references to known entities, which allows them to draw on all the structured data associated with that entity. If your brand is not established as a recognised entity, or if your entity data is thin and contradictory, AI engines cannot confidently include you in their responses. Entity establishment is not optional for AI visibility — it is the starting point.

What is schema markup, and how does it connect to AI engines?

Schema markup is structured data code — usually JSON-LD — embedded in your website's pages that describes what each page contains using a standardised vocabulary from Schema.org. It tells AI crawlers and search engines whether a page is about a product, a service, an article, a FAQ, or an organisation. AI engines use schema markup alongside entity data to form a richer, more reliable understanding of your content. Invalid or missing schema means AI engines have to infer your page's meaning rather than read it directly — and inference is less accurate and less reliable than structured data.

Which 25 sources does the entity audit cover?

The 25 sources include Wikipedia (English and Nepali editions), Wikidata, Google Knowledge Graph, Google Business Profile, Crunchbase, LinkedIn Company Pages, Facebook Business Pages, industry-specific directories relevant to your category, Nepali government business registries, local news archives and press coverage, Apple Maps, Bing Places, industry association listings, review platforms, and additional structured databases that AI engines are known to consult during entity resolution. The exact source list is adapted to your business category and geography.

How long does the audit take?

The Entity & Schema Audit is completed within 7 to 10 business days from the date we receive your brand information brief. Delivery includes the full coverage map, schema crawl report, validation report, and priority fix roadmap. If your website is very large or your entity footprint is unusually complex, we will advise on timeline adjustments before starting.

What does the audit cost?

The Entity & Schema Audit is priced from NPR 45,000 for a standard business. Pricing varies based on website size and the complexity of your entity footprint. We provide a fixed-fee quote after a brief scoping call. There are no ongoing fees — this is a one-time diagnostic engagement.

What happens after the audit is delivered?

After delivery, you have three options. You can implement the priority fix roadmap using your own team or developer, referring to the detailed correction instructions we provide. You can engage Ignited Nepal to implement the fixes as a separate project, working through the roadmap in priority order. Or you can use the audit as the foundation for a broader AI visibility programme, feeding the findings into a GEO content strategy, entity building campaign, or brand answer accuracy work. The audit is designed to stand alone as a diagnostic, but it also integrates directly with every other AI visibility service we offer.

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 entity data either works for you or against you. Find out which.

Every day that your brand operates with incomplete entity data or invalid schema markup is a day AI engines are forming their understanding of your business from fragments, contradictions, and gaps. The Entity & Schema Audit tells you exactly what those problems are and exactly what to do about them — in priority order, with no guesswork.

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Run an AI Visibility Audit