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B2B buyers and SaaS procurement teams are running vendor research in ChatGPT before they ever visit your website. If the AI description of your company is wrong — outdated product positioning, incorrect company details, or confusion with a competitor — you are being disqualified before the conversation starts. We audit what AI engines say about your brand and correct it at the source.
The wrong description
Conflated with a competitor
Describing the old version of you
You have no idea what AI says
Buyers use ChatGPT to answer questions like "compare [your category] vendors" or "is [your company] good for [use case]." If the AI draws on outdated Crunchbase data, early press coverage, or stale G2 summaries, its vendor assessment is wrong — and the buyer acts on it.
SaaS categories often have multiple companies with similar names, overlapping positioning, or shared terminology. AI models conflate entities, blending your company's facts with a competitor's. Buyers researching you get a description that is partially about someone else.
Your product has evolved significantly — new features, new use cases, new target markets — but AI engines describe version 1.0. The sources they trust most were written when you first launched and have not been updated. The gap between what AI says and what you actually offer widens with every product cycle.
Early-stage or mid-market B2B companies often lack the authoritative source depth that AI engines require to generate accurate responses. Without Wikipedia coverage, Wikidata entries, and authoritative analyst mentions, AI engines fabricate or omit — both outcomes losing you deals.
We query 12 AI engines — including ChatGPT, Perplexity, Gemini, Claude, and Copilot — using your company name, competitor comparison queries, and category queries relevant to your US market segment. Every response is documented. Every inaccuracy is flagged. You receive a complete Brand Answer Audit Report.
Every inaccuracy is categorised and diagnosed. We identify exactly which source is feeding wrong information into AI vendor evaluations — which crawled page, which outdated record, which stale analyst summary — so corrections are targeted and durable.
We update the authoritative records that AI engines draw on: Wikipedia, Wikidata, Crunchbase, and relevant US tech and business directories. Corrections reflect your current product, positioning, and company details.
Accurate brand mentions in tier-1 US tech media, industry analysts, and authoritative business publications give AI engines reliable, citable information. We earn placements that LLMs draw on when evaluating vendors in your category.
We build the structured data signals that allow AI engines to distinguish your brand from similarly positioned US competitors — ensuring vendor evaluations describe your company, not a blended entity.
Every month, we re-audit your brand answers across 12 AI engines, track accuracy improvement over time, and deliver a Monthly Brand Accuracy Report. You maintain ongoing visibility into how AI describes your company to buyers.
Brand answer accuracy is the degree to which AI engines — ChatGPT, Perplexity, Gemini, and others — describe your company correctly when buyers use them for vendor research and evaluation.
B2B buyers use ChatGPT to shortlist vendors before making contact. If the AI description of your company is wrong — outdated product positioning, incorrect company stage, or confusion with a competitor — buyers remove you from the shortlist before reaching out. The lost deal never registers in your pipeline data.
AI engines draw on web data crawled at various points in the past. Outdated Crunchbase entries, early-stage press coverage, and stale analyst summaries feed inaccurate information into vendor evaluation responses. Without active correction, those inaccuracies compound over time.
We query 12 AI engines using your company name, competitor comparison prompts, and category-level vendor queries. Every response is documented. Every inaccuracy is identified, categorised, and diagnosed. You receive a Brand Answer Audit Report with a hallucination log and source diagnosis.
We update the source records AI engines draw on — Wikipedia, Wikidata, Crunchbase, and US business directories — and earn accurate brand mentions in authoritative tech media and analyst publications that LLMs reference during vendor evaluation.
Source corrections typically begin reflecting in AI responses within four to twelve weeks. Retrieval-augmented engines like Perplexity update faster; models with longer training cycles take more time.
Pricing depends on audit scope, the volume of inaccuracies identified, and the scale of authoritative source building required. Contact us at us@ignitednepal.com for a scoped proposal with USD pricing.
Yes. Monthly monitoring covers re-auditing brand answers across 12 engines, tracking accuracy improvement over time, and flagging new inaccuracies in a Monthly Brand Accuracy Report delivered to your team.
The vendor evaluation is happening in ChatGPT before your sales team knows the buyer exists. If the AI description of your company is wrong, the deal is lost before the conversation starts. We audit what AI engines say, correct inaccuracies at source, and monitor accuracy every month.
Ignited Nepal — Growth Engineering Company. AI visibility specialists for B2B and SaaS businesses in the US market.