ENTITY TRUST BUILDING

AI Engines Don't See Your Brand the Way Your Clients Do.

ChatGPT, Perplexity, Google Gemini, and Bing Copilot don't evaluate your marketing materials or browse your website to form an opinion of your organisation. They read structured entity data — Wikipedia articles in English and French, Wikidata records, Google Knowledge Graph entries, and citations in authoritative Canadian business directories. If that data is thin, inconsistent, or absent in either official language, AI engines either omit your brand from relevant answers or describe it incorrectly to the prospects who are asking about your sector. For Canadian businesses operating in both English and French markets, the entity challenge is doubled: you need coherent entity records in both languages, across both regional and national directory sources, to be recognised as a credible structured entity by the AI systems your clients increasingly rely on. Ignited Nepal builds the bilingual entity infrastructure that makes your Canadian organisation visible, accurate, and trusted — from Vancouver to Quebec City.

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

WHO THIS IS FOR

Bilingual and Quebec-market businesses — companies operating under Quebec's language requirements or serving both English and French Canadian markets face a structural visibility gap that English-only entity records cannot bridge. AI systems responding to French-language queries consult French Wikipedia (fr.wikipedia.org), French-language Wikidata descriptions, and Quebec-specific directories. Without coherent French-language entity records, these systems either cannot find your organisation or retrieve incomplete information.

Professional and financial services firms — accountants, lawyers, financial advisers, and consultants operating under CPA Canada, the Law Society, or equivalent provincial professional bodies whose clients conduct AI-assisted due diligence before engaging services. A well-structured entity record in authoritative Canadian sources is a prerequisite for appearing credible in those research processes.

Technology companies and scale-ups — Canadian tech companies raising capital, entering enterprise sales cycles, or expanding internationally whose investors, enterprise clients, and partners use AI research tools to evaluate them before meetings. A thin or absent entity record in Wikipedia, Wikidata, and the Canadian Business Register is a gap that sophisticated parties notice.

Companies in federally regulated industries — telecommunications, banking, insurance, broadcasting, and transportation businesses regulated at the federal level whose entity records need to reflect their regulatory standing accurately in the sources AI systems reference, particularly as AI adoption in regulatory compliance research accelerates.

What's broken

WHAT'S BROKEN

Your French-language entity record doesn't exist — or is a machine translation.

French Wikipedia (fr.wikipedia.org) and French-language Wikidata attributes are distinct from their English counterparts and require separate, properly written content. Many Canadian companies have either no French-language entity record at all, or a poorly machine-translated version that French Wikipedia editors flag for quality issues. AI systems responding in French to queries about your industry find nothing coherent, or find content that signals low credibility.

Your Canadian Business Register presence is not feeding your entity signals.

The Canadian Business Register (Registre des entreprises du Canada) is an authoritative federal source that AI systems and Knowledge Graph algorithms recognise as a verification anchor. When your entity data across other citations does not align precisely with your Canadian Business Register record — including your legal name in both official languages where applicable — you produce entity signal fragmentation that suppresses visibility.

Inconsistent data across provincial and national directories is undermining your entity coherence.

Your Yellow Pages Canada listing, your BBB Canada profile, your provincial corporate registry entry, and your website all describe your business with slight variations in name format, address, and description. To a Knowledge Graph algorithm processing multiple data points about the same entity, these inconsistencies introduce uncertainty, and uncertain entities are surfaced less confidently across AI-generated answers.

You are invisible to French-language AI research in Quebec and international Francophone markets.

Quebec's AI adoption in professional and business research is substantial and growing. When a Quebec procurement manager, investor, or partner asks an AI assistant in French about companies in your sector, the AI consults French Wikipedia and French-language Wikidata first. If your entity record doesn't exist in those sources, you are not part of that conversation — regardless of how strong your English-language presence is.

What we engineer

WHAT WE DO

Bilingual entity infrastructure, English and French

Ignited Nepal's Entity Trust Building service for Canada builds a complete bilingual entity infrastructure — English and French — that makes your organisation a recognised, verified entity in every authoritative source that AI engines and search algorithms consult when serving Canadian users.

Canada-specific entity gap audit

We begin with a Canada-specific entity gap audit that examines your representation in English Wikipedia, French Wikipedia, Wikidata (both English and French attributes), Google Knowledge Graph (Canada), Google Business Profile (bilingual), Yellow Pages Canada, BBB Canada, the Canadian Business Register, and 20+ additional sector-relevant and regional directories. From that audit, we design a bilingual entity architecture and execute the full build.

English Wikipedia

English Wikipedia article creation or substantive improvement, written to Wikipedia's notability and neutrality standards with citations to Canadian sources including Canadian Business Register filings, major Canadian press coverage, and industry publications

French Wikipedia

French Wikipedia (fr.wikipedia.org) article creation or substantive improvement, written in proper French to fr.wikipedia.org editorial standards, with citations to French-language Canadian sources — La Presse, Le Devoir, Les Affaires, Radio-Canada, and applicable Quebec regulatory or industry sources

Wikidata entity enrichment

Wikidata entity enrichment with complete bilingual attributes: English and French labels (étiquettes), English and French descriptions (descriptions), English and French aliases (alias), plus structured data fields including business registration identifiers, industry classification, founding date, headquarters address, official websites in both languages, key personnel, and subsidiary or parent entity relationships

Google Knowledge Panel

Google Knowledge Panel claiming and enhancement with accurate bilingual content, correct business categorisation for the Canadian market, and verified contact data

Directory citation campaign

Directory citation campaign across 20+ authoritative sources including Canadian Business Register, Yellow Pages Canada, BBB Canada, Bing Places Canada, Apple Maps Canada, provincial business registries (Ontario, Quebec, BC, Alberta as applicable), sector-specific industry association directories, and Quebec-specific directories relevant to your industry

Bilingual citation consistency audit

Bilingual citation consistency audit ensuring your English legal name, French legal name (where applicable), registered address, founding date, and business description are uniform across all citations

Run an AI Visibility Audit →
Common questions

FAQ

What is a brand entity and why does it matter for Canadian businesses specifically?

A brand entity is a structured, machine-readable record that defines your organisation as a distinct, identifiable subject — verified attributes, cross-referenced identifiers, and relationship data that AI engines and search algorithms can consult with confidence. For Canadian businesses, entity records matter in a distinct way: the bilingual information environment means your entity needs to be coherently documented in both English and French-language sources to be recognised as a complete, credible entity by AI systems serving users in either official language. A company well-documented in English Wikipedia but absent from French Wikipedia is invisible to French-language AI queries — including those from Quebec's substantial enterprise market.

Does every Canadian company qualify for a Wikipedia article in English and French?

No. Both English Wikipedia and French Wikipedia apply notability standards that require significant coverage in reliable, independent sources — national or regional press, industry publications, regulatory filings reported on by credible outlets. Companies with limited third-party coverage in either language may not currently qualify for a standalone Wikipedia article in that language. In those cases, we identify the path to qualification, assess what Wikidata enrichment and Knowledge Graph entries can achieve in the interim, and are direct about what is immediately achievable. We will not pursue a Wikipedia article that the editorial community would decline — that wastes time and can create editorial problems that complicate future attempts.

How long does the process take for a bilingual Canadian entity build?

The Entity Gap Audit and Entity Structure Plan are completed within two weeks. The Core Entity Build — English Wikipedia, French Wikipedia, Wikidata enrichment, and Knowledge Graph submission — takes four to six weeks, with the bilingual complexity adding time relative to a single-language build. The Directory Citation Campaign runs in parallel and is complete within four weeks. Most Canadian clients should plan for a total of ten to fourteen weeks from engagement to a fully deployed bilingual entity infrastructure. French Wikipedia editorial review timelines can vary as they depend on volunteer editorial processes; we manage that process throughout.

What does Entity Trust Building cost in Canadian dollars?

Entity Trust Building for Canadian organisations is priced from CAD $6,000 for the complete bilingual programme, covering the audit, entity architecture design, English and French Wikipedia work, Wikidata enrichment, Knowledge Graph submission, 20+ directory citations, and the first quarterly monitoring review. Organisations with more complex entity structures — Quebec-specific legal entity requirements, multiple provincial registrations, regulated industry memberships, or subsidiary relationships — are priced on the basis of the audit findings. All pricing is confirmed in writing before any work begins.

Which Canadian directories carry the most weight for entity trust signals?

The highest-weight Canadian directory sources for entity trust are those AI engines and Knowledge Graph algorithms treat as authoritative federal or provincial verification anchors. The Canadian Business Register is foundational — it is the federal reference point that other citations should align with. Yellow Pages Canada and BBB Canada carry significant weight for general business entity signals. Provincial corporate registry records (Ontario Business Registry, Registre des entreprises du Québec, BC Registry, ABCA) are important for regional entity coherence. For Quebec and Francophone markets, alignment between your French Wikipedia article, your French-language Wikidata descriptions, and French-language directory citations creates a coherent entity signal that AI systems operating in French specifically value.

How does entity building improve AI visibility, and how quickly does it take effect?

AI engines reference structured, authoritative data sources when constructing answers. When your entity record is coherent and well-documented across English Wikipedia, French Wikipedia, Wikidata, the Canadian Business Register, and major directories, AI systems have the structured data they need to include your brand in relevant answers in both languages. The effect is not immediate — Knowledge Graph updates and AI training data refreshes operate on their own schedules — but the foundation you build now determines your entity's representation in AI systems for years. Directory citations typically begin influencing Knowledge Graph data within four to eight weeks. Wikipedia and Wikidata improvements flow into AI training data on longer cycles, but the entity confidence signals they produce begin influencing search algorithm behaviour relatively quickly.

Start here

The bilingual entity gap is real — and most of your Canadian competitors haven't closed it yet.

French-language entity records, Quebec-specific directory citations, and bilingual Knowledge Panels take time to build and mature. Start with a clear picture of where your entity infrastructure stands in both languages.

Run an AI Visibility Audit →

Run an AI Visibility Audit