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AI VISIBILITY ENGINEERING
B2B buyers use ChatGPT, Perplexity, and Gemini to research vendors before they get on a call. If your brand is absent from Wikipedia, Wikidata, Crunchbase, G2, Capterra, and the structured sources LLMs actually query — you are invisible in the research phase that precedes every deal. We are the entity trust building agency that builds the structured brand footprint B2B AI searches depend on.
B2B buyers are using AI to build their vendor shortlists before they ever visit your website. Your entity footprint determines whether you make the list.
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/ This is for you if
Strong SEO, invisible to AI buyer research — Your content ranks well, your demand gen is running, and your SEO metrics look solid. But when a B2B buyer asks ChatGPT to compare vendors in your category, you are not in the answer. That is an entity gap — not a content gap.
The founder who searched their brand in ChatGPT — You asked ChatGPT about your company and got no response, a thin description, or a summary that described a competitor. Your entity footprint in the sources LLMs query — Wikipedia, Wikidata, Crunchbase, analyst sites — is not strong enough to generate a confident brand answer.
The CMO who knows entity SEO is real but untouched — You understand that Knowledge Graph optimisation and entity SEO exist. You have flagged it internally. But no one has built your entity footprint systematically — because finding an agency that does this specifically for B2B and SaaS brands is genuinely difficult.
The scaling company whose entity records are out of date — You have raised rounds, expanded your product, entered new verticals. AI engines are still describing your Series A-era positioning because your Wikidata record, Crunchbase profile, and Wikipedia article were never updated to reflect the current business.
/ What's broken
Wikipedia is the single most influential entity signal for AI model recognition. B2B brands without a Wikipedia article are systematically underrepresented in AI-generated vendor comparisons, category answers, and analyst-style summaries that B2B buyers use to build shortlists.
Wikidata is the structured database LLMs query for entity facts. For B2B companies, this includes founding date, HQ, funding rounds, product category, and identifiers. Missing or incomplete Wikidata records mean AI engines either cannot cite you or cite you incorrectly in the responses your buyers read.
B2B buyers and the AI engines they use rely heavily on structured review and analyst platforms. If your Crunchbase profile has your old category, your G2 listing has the wrong product description, and your Capterra entry is unclaimed — AI engines receive contradictory signals and reduce their confidence score for your brand.
Gartner, Forrester, G2, Capterra, TrustRadius — these are the structured sources B2B buyers trust and that AI models have been trained on. Thin presence here means your brand is underrepresented in exactly the AI-generated answers your buyers are reading during the research phase of the buying cycle.
/ What we engineer
Each workstream is built by the team that audits it, and wired into the rest of your growth system.
A complete audit of your B2B entity footprint: Wikipedia, Wikidata, Google Knowledge Graph, Crunchbase, G2, Capterra, Gartner peer insights, TrustRadius, and 20+ additional sources. Every gap is documented with priority scores weighted for B2B buyer research patterns.
We write or update your Wikipedia article to meet notability standards for B2B technology and services companies. For SaaS and B2B brands, Wikipedia is the foundational entity signal — the source AI engines cite when generating vendor overviews that appear in buyer research.
We create or complete your structured Wikidata record — founding date, HQ, industry and product category, funding history, key identifiers including SEC filings and GLEIF LEI. This machine-readable record is what LLMs query for structured facts about your company.
We claim and verify your Knowledge Panel, ensure all B2B-relevant fields are accurate, and align the panel data with your Wikipedia and Wikidata records. A verified Knowledge Panel raises AI confidence and improves how your brand appears in AI-generated category answers.
Structured citations across 20+ B2B-relevant sources: Crunchbase, G2, Capterra, Gartner Peer Insights, TrustRadius, LinkedIn, Bloomberg, Pitchbook, industry analyst directories, and trade association listings. Each citation is a data point that builds AI confidence in your brand.
We standardise your brand name, product category, company description, and identifiers across Crunchbase, G2, Capterra, Wikipedia, Wikidata, and all other sources — eliminating the conflicting signals that cause AI engines to generate inconsistent or inaccurate brand summaries.
/ What changes
/ Common questions
Straight answers on scope, timelines and cost. Anything else, ask us directly.
Ask us directlyAn entity trust building agency creates and standardises the structured brand data that AI engines use to recognise, describe, and cite a company. For B2B and SaaS brands, this means building presence in Wikipedia, Wikidata, Crunchbase, G2, Capterra, and the other structured sources LLMs query when generating vendor research answers.
AI engines generate vendor comparisons and category answers by querying structured data sources — Wikipedia, Wikidata, Crunchbase, G2, Capterra. If your entity footprint in those sources is thin or inconsistent relative to your competitors', AI engines cite them with more confidence and frequency than they cite you.
Crunchbase is one of the primary structured sources LLMs use for company facts — founding date, funding, product category, team size. A complete, accurate, current Crunchbase profile is a direct input into AI-generated answers about your company. An incomplete or outdated Crunchbase profile produces inaccurate AI citations.
G2 and Capterra are among the sources AI engines have been trained on for B2B software category information. A claimed, complete, and accurately categorised profile on both platforms contributes to the structured data AI engines use when generating software comparisons and vendor shortlists.
US B2B entity trust building engagements start from USD 4,500 for a full entity build, depending on the scope identified in the gap audit. Contact info@ignitednepal.com for a proposal based on your specific situation.
Core entity assets — Wikipedia, Wikidata, Knowledge Panel — typically propagate into AI-generated answers within six to twelve weeks of completion. B2B platform citations (Crunchbase, G2, Capterra) compound over two to three months as AI engines re-index and re-weight the updated data.
Entity trust building targets the structured, authoritative data sources that AI engines query — Wikipedia, Wikidata, and platform directories — rather than the website-level signals that search engines crawl. It is a separate discipline from on-page SEO, backlink building, and content strategy, and requires different expertise.
Wikipedia notability for SaaS companies requires third-party coverage: press mentions, analyst coverage, notable funding rounds, industry recognition. We assess notability as part of the entity gap audit. For brands that are not yet Wikipedia-notable, we build the other entity layers first — Wikidata, Crunchbase, G2, Capterra — and advise on the notability path for Wikipedia when the threshold is reachable.
Start here
B2B buying has changed. The research phase happens in ChatGPT, Perplexity, and Gemini — before buyers visit your website, before they fill in a form, before they get on a call. The brands that appear in that research phase are the brands with a structured entity footprint across the sources AI engines query. We build it from the ground up: Wikipedia article, Wikidata record, Google Knowledge Panel, and 20+ structured citations across Crunchbase, G2, Capterra, Gartner, and the analyst and review sources B2B buyers trust — all standardised for consistency so AI engines can cite you with confidence.
Ignited Nepal — Growth Engineering Company. Entity trust building for B2B and SaaS brands that need to appear in AI-generated buyer research.Ignited Nepal
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