ENTITY & SCHEMA AUDIT

When AI Engines Look Up Your Business, What Do They Find?

ChatGPT, Gemini, Perplexity, and the AI tools embedded in enterprise search and procurement workflows consult structured knowledge data before they consult your website. They look for your entity record — the accumulated set of data points across Wikipedia, Wikidata, business registries, and authoritative directories that tells them what your business is, where it operates, and whether it merits citation. If that record is incomplete, inconsistent, or missing entirely, AI engines do not surface you. And they do not tell you why.

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

WHO THIS IS FOR

Mid-market and enterprise businesses competing in AI-generated answers. You are investing in SEO and content but noticing that AI-generated answers increasingly cite your competitors rather than you. The gap is rarely in the quality of your content — it is in the completeness and consistency of your entity data and the accuracy of your schema markup, which AI engines assess before they assess anything on your website.

Bilingual businesses serving both English and French Canadian markets. Operating in Canada's bilingual environment means maintaining coherent entity records in both languages. English Wikipedia, French Wikipedia, English Wikidata entries, and French-language directory listings all contribute to the knowledge graph that AI engines draw from. If your French-language entity data is thinner or less consistent than your English-language data, AI engines serving Quebec and Acadian markets will deprioritise you in favour of competitors with stronger bilingual entity coverage.

Regulated businesses with Canadian compliance directories. Financial advisers (IIROC, MFDA registers), lawyers (Law Society provincial registries), accountants (CPA Canada provincial bodies), and healthcare providers operate in sectors where Canadian regulatory directory entries carry specific authority weight with AI systems. Incomplete or mismatched entries in these directories create authority breaks that no amount of website optimisation can repair.

E-commerce and retail businesses using product schema. For businesses selling products online, Product schema, Offer schema, and AggregateRating markup are the structured data that AI shopping tools and AI product comparison features read first. Absent or invalid product schema means AI tools cannot accurately describe, compare, or recommend your products in generated answers.

What's broken

WHAT'S BROKEN

English and French entity records tell different stories.

The most common finding in Canadian audits is a material discrepancy between English and French entity data. English Wikipedia may have an article; French Wikipedia has a stub or nothing. English-language directories are well-populated; French-language equivalents are thin or outdated. The Canadian Business Register entry may not align with provincial registry data. Each inconsistency reduces AI engine confidence in your entity, and AI tools serving French-language queries experience this most acutely.

Schema markup is present but not performing.

Most Canadian business websites have some schema markup installed, typically through a CMS plugin or a developer who added it during the original site build. But plugins implement markup to a generic standard, not to the specific requirements of your business type. An accounting firm does not need the same schema as a restaurant — yet both may be using an identical plugin-generated Organisation block that omits the professional service classifications, regulatory body affiliations, and service area declarations that AI engines specifically look for.

The Canadian Business Register and BBB Canada entries are incomplete or inconsistent.

These two sources — plus provincial business registries — are among the first places AI engines check when assessing the legitimacy and authority of a Canadian business entity. Outdated addresses, missing business descriptions, dormant listings, and mismatched legal names all create signals that reduce AI confidence in your entity.

Product and service schema is absent on pages that drive revenue.

AI tools handling shopping queries, service comparisons, and product research draw directly from structured data rather than page text. If your product pages, service pages, and category pages lack valid schema markup, AI tools cannot include your products or services in comparative generated answers — regardless of how detailed your page content is.

What we engineer

WHAT WE DO

25-source bilingual entity map

a structured inventory across Wikipedia (English), Wikipedia (French), Wikidata (English and French entries), Google Knowledge Graph, Google Business Profile, the Canadian Business Register, BBB Canada, Crunchbase, LinkedIn Canada, provincial business registries (Ontario, British Columbia, Alberta, Quebec, and others relevant to your operations), IIROC/MFDA registers (if applicable), CPA Canada provincial directories (if applicable), Law Society registries (if applicable), Bing Canada, Apple Maps Canada, industry association directories, news archives (Canadian Press, Globe and Mail, Le Devoir), PR Newswire Canada, Cision Canada, data aggregator feeds, and additional sector-specific sources identified during scoping.

Entity data quality scorecard

each source scored for completeness, accuracy, and consistency, with bilingual coherence assessed as a separate dimension.

Full schema markup crawl

extraction of every structured data block on your domain, with coverage assessed across both English and French language sections of the site.

Schema validation and error report

every markup block tested against Schema.org and Google requirements, with errors ranked by AI visibility impact.

Competitive entity benchmark

your entity coverage compared against three direct competitors in the Canadian market.

Priority fix plan

a sequenced, ranked action list covering both entity gap remediation and schema error correction, with specific instructions for Canadian-source updates and bilingual schema improvements.

Run an AI Visibility Audit →
What changes

WHAT CHANGES

Before
After
Before AI engines begin surfacing your business in both English and French generated answers.
After Coherent bilingual entity data gives AI tools serving queries in either language the confidence to cite you accurately. For businesses competing in Quebec or serving bilingual audiences nationally, this represents a significant visibility gain that content-only approaches cannot produce.
Before Your business description stabilises across AI platforms.
After The inconsistencies, outdated descriptions, and inaccurate details that AI tools have been drawing from fragmented entity data are replaced by a consistent, accurate record. ChatGPT, Gemini, Perplexity, and AI-enhanced search tools all draw from the same corrected knowledge layer.
Before Product and service schema starts contributing to AI shopping and research answers.
After When Product, Service, and Offer schema is correctly implemented and validated, your products and services become visible to the AI tools handling comparison, recommendation, and research queries. This is a traffic source that does not exist for businesses without valid schema markup.
Before The compliance directory authority gap closes.
After For regulated businesses, validated entries in IIROC, CPA Canada, Law Society, and other Canadian regulatory directories — properly linked and consistently described — add an authority signal that AI engines weight heavily when deciding whether to cite a professional services business.
Common questions

FAQ

What is a business entity in the context of AI visibility?

A business entity is the structured data record that AI engines consult to understand your organisation — not your website text, but the accumulated data points across Wikipedia, Wikidata, business registries, and authoritative directories that collectively define what your business is and whether it can be trusted as a source. AI engines form a confidence score about your entity based on the completeness and consistency of this record, and that score determines whether they cite you in generated answers.

Why does schema markup matter for AI engines?

Schema markup is structured code on your website that tells machines — not just humans — exactly what your content describes. When AI engines process a page with valid schema markup, they can extract precise facts: this business is a law firm, it operates in Toronto and Vancouver, it is a member of the Law Society of Ontario, it specialises in corporate litigation. Without that markup, AI engines must infer these facts from prose, which they do imprecisely and inconsistently.

Why is bilingual entity data specifically important for Canadian businesses?

AI engines serving French-language queries draw primarily from French-language sources: French Wikipedia, French Wikidata entries, French-language directory listings. If your French-language entity record is thinner or less accurate than your English-language record, AI tools serving Quebec and other French-speaking markets will assess your entity as lower-authority than your English-market competitors — even if your actual business presence in both markets is equivalent.

Which 25 sources does the audit check?

The full source set covers: English Wikipedia, French Wikipedia, Wikidata, Google Knowledge Graph, Google Business Profile, Canadian Business Register, BBB Canada, Crunchbase, LinkedIn, provincial business registries for all relevant provinces, applicable regulatory body directories (IIROC, MFDA, CPA Canada, Law Society provincial bodies), industry association directories, Bing Canada, Apple Maps Canada, Canadian Press archive, major English and French press archives, PR Newswire Canada, Cision Canada, data aggregator feeds, academic citation databases, and sector-specific sources identified during scoping.

How long does the audit take and what does it cost?

The standard audit delivers in ten to fourteen working days. Pricing starts at CAD $2,400 for single-location businesses operating in one language. Bilingual audits covering both English and French entity data, or multi-province businesses with multiple regulatory directory requirements, are quoted following a brief scoping call.

What should we do with the priority fix plan once we receive it?

The fix plan is written to be actionable without further consultation — each item specifies what to change, where, and how. Your internal marketing, content, or development team can implement most items directly. For organisations that prefer managed implementation, Ignited Nepal offers a separate implementation service and an ongoing monitoring retainer.

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

Your entity data exists across dozens of sources right now. Most of it was never designed for AI engines.

The businesses that appear consistently in AI-generated answers in the Canadian market have done the foundational work of building complete, consistent, bilingual entity records and valid schema markup. The audit shows you exactly where your data stands, in both languages, against the full set of sources AI engines actually check.

Run an AI Visibility Audit →

Run an AI Visibility Audit