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ChatGPT, Perplexity, and Gemini generate descriptions of your business every day. If those descriptions are wrong — outdated, conflated with a competitor, or simply fabricated — you are losing credibility with buyers who never tell you. Brand Answer Accuracy audits, corrects, and monitors what AI says about your brand.
The wrong description — You searched your company on ChatGPT and found it describing services you no longer offer, a location you left years ago, or a founding story that is simply not true. You did not know where that information came from or how to change it.
Confused with a competitor — When buyers ask AI about your sector, your brand gets mentioned alongside — or confused with — a competitor. The AI treats both as interchangeable. Deals go to the competitor because the AI positioned them first.
Stuck in the past — Your brand underwent a pivot, a rebrand, or a significant shift in positioning. AI engines still describe the old version. Press releases and website updates did not filter through to the large language models drawing on stale data.
Flying blind — You are a marketing director responsible for brand perception. You have no visibility into what AI engines say about your business. You suspect the answers are not ideal, but you have no audit, no baseline, and no process to monitor it.
ChatGPT and similar engines draw on web data that may be years old and was never corrected. If an early news article described your company inaccurately, or a third-party directory has outdated information, those errors persist in AI training data and live responses indefinitely — unless someone corrects the authoritative sources.
Entity disambiguation is a known challenge for large language models. If your brand name is similar to a competitor's, or if both operate in the same category, AI engines may conflate the two — attributing the competitor's products, clients, or positions to you. This is an entity footprint problem, not a content quality problem.
Authoritative sources — Wikipedia, Wikidata, Crunchbase, industry databases — update slowly. When your brand evolves faster than those records, AI continues describing your former positioning, team, or service set. Updating your website is insufficient. The underlying authoritative records must be corrected.
For brands with a thin entity footprint — few structured citations, no Wikipedia entry, minimal tier-1 media coverage — AI engines either omit the brand entirely or generate unreliable, speculative descriptions. The engine has too little quality signal to produce an accurate answer.
We query 12 AI engines — including ChatGPT, Perplexity, Gemini, Claude, Copilot, and Grok — with the questions your buyers are likely asking. Every response is documented, scored for accuracy, and flagged for inaccuracies. You receive a complete Brand Answer Audit with a hallucination log.
Every inaccuracy identified in the audit is categorised by type — fabricated fact, conflation, outdated data, missing entity signal — and traced to its probable source. You know exactly what is wrong, why it is wrong, and where it originated.
We update the authoritative records that AI engines rely on: Wikipedia, Wikidata, Crunchbase, Companies House references, and relevant industry databases. Corrections are documented in a Correction Log so you have a full audit trail.
Accurate brand mentions in tier-1 media, trade publications, and high-authority directories give AI engines reliable signal to draw on. We place your correct brand story in the sources large language models weight most heavily.
We build the structured signals — consistent name, sector, geography, product, and founder data across authoritative sources — that help AI engines distinguish your brand from similarly named or positioned competitors. Your entity becomes unambiguous.
AI responses change as models are updated and new data is ingested. We re-audit your brand answers monthly, track improvement over time, and flag new inaccuracies before they compound. You receive a Monthly Brand Accuracy Report.
Brand answer accuracy is the degree to which AI engines — ChatGPT, Perplexity, Gemini, and others — describe your brand correctly when asked about it. If an AI gives a buyer wrong, outdated, or misleading information about your company, that inaccuracy influences their decision before you ever speak to them.
The most reliable method is a structured brand answer audit: querying multiple AI engines with the questions your buyers are likely to ask, then documenting and analysing every response. We audit 12 engines as part of our Brand Answer Accuracy service, covering the platforms your UK audience is most likely to use.
Yes. Most AI inaccuracies trace back to specific authoritative sources — Wikipedia entries, Crunchbase profiles, directory listings, or early press coverage — that contain wrong information. Correcting those sources and building accurate signal in tier-1 media gives AI engines better data to draw on. It is not immediate, but it is addressable.
In legal, financial services, and healthcare, an AI description that misrepresents your firm's authorisations, services, or qualifications can mislead prospective clients before any formal engagement. That carries professional and reputational risk that is distinct from general brand inaccuracy. Accurate AI descriptions are not optional for regulated businesses — they are a risk management matter.
Correction of structured authoritative sources — Wikipedia, Wikidata, Crunchbase — typically begins to influence AI responses within four to twelve weeks, depending on the model's data refresh cycle. Authority building through media placements compounds over months one to three. Monthly monitoring tracks the improvement curve so you have evidence of progress.
That is an entity footprint problem. AI engines have insufficient reliable data about your brand to generate a confident response. The solution is building structured, consistent brand data across authoritative sources — Wikipedia entry, Wikidata entity, Crunchbase profile, tier-1 media mentions — so AI engines have enough quality signal to include and describe your brand accurately.
Brand answer accuracy services are priced from £1,800 per month for ongoing monitoring, which includes the monthly audit across 12 engines and a Brand Accuracy Report. Initial audit and correction work is scoped separately based on the number and complexity of inaccuracies identified. Contact us for a scoped proposal.
Traditional SEO optimises your content to rank in search engine results pages. Brand answer accuracy optimises the data that AI engines draw on when generating direct answers about your brand — a fundamentally different mechanism. Search rankings depend on on-page signals; AI accuracy depends on the quality and consistency of your authoritative source data and entity footprint.
If buyers are researching your business through ChatGPT, Perplexity, or Gemini before they contact you, what they read matters. A Brand Answer Accuracy audit gives you a documented view of every inaccuracy across 12 AI engines, a diagnosis of where each error originates, and a structured correction programme. Ignited Nepal is a growth engineering company. We do not run campaigns — we fix the data infrastructure that AI draws on.
Ignited Nepal — Growth Engineering Company