A SaaS platform that tracks how brands and topics appear within the answers of major AI models (ChatGPT, Perplexity, etc.). It provides analytics on brand visibility, citation sources, and sentiment, offering tools to help companies optimize their content to be featured more prominently in AI-generated responses.
Buyers increasingly ask AI assistants such as ChatGPT and Perplexity for recommendations, like which CRM suits a small business or which accountant in Brisbane handles crypto tax. The answers name a handful of brands and cite particular sources. Marketing teams have almost no visibility of whether their business appears, how it is described or which competitors are recommended instead, because none of this shows up in traditional search rankings or analytics.
Manual spot checks are unreliable. Answers vary by wording, location, time and model, so asking a question once gives a misleading picture. Without systematic tracking, teams cannot tell whether content changes, PR coverage or review activity affect how AI assistants talk about them. They also miss inaccurate descriptions, such as outdated pricing or discontinued products, which may be repeated to many potential customers without anyone noticing.
The business defines a set of prompts buyers would realistically ask, grouped by topic, location and funnel stage, plus the competitor brands to track.
Scheduled jobs send those prompts to multiple AI models through their official APIs, repeating runs to capture the variation in answers.
Responses are parsed to record which brands are mentioned, their position, the sentiment of the description and the sources or links cited.
Results are stored and presented in a BI dashboard showing visibility trends, share of mentions against competitors and frequently cited domains.
An analysis layer suggests content and PR actions, such as sources worth being cited in or pages that need clearer factual information.
A software company uses the platform to see how often their product is mentioned by ChatGPT versus competitors when users ask 'what is the best CRM for small businesses?'. The platform provides a dashboard with visibility trends and suggests content strategies to improve their ranking in AI answers.
LLM APIs (OpenAI, Claude), Web Scraping, Data Analytics, BI Dashboards, SaaS Architecture
Querying AI services should be done through their official APIs within their terms of service; automated scraping of consumer chat interfaces is commonly prohibited. API answers may differ from what a person sees in the consumer app, so explain this limitation to users of the dashboard. Include Australian locations and terminology in prompts, since answers can differ by region. Content created to improve AI visibility must be accurate and must not disparage competitors misleadingly, as the Australian Consumer Law applies. Planting fake reviews or testimonials to influence AI answers breaches ACCC guidance.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
It can estimate how often and how prominently you are mentioned across many runs of relevant prompts, which is more useful than a single ranking. AI answers change with wording and over time, so the platform reports visibility trends and share of mentions rather than a fixed position.
Commonly suggested steps include publishing clear, factual pages about your products and pricing, earning coverage on sources that AI assistants cite, keeping business listings consistent and encouraging genuine reviews. The platform helps you see which sources matter in your category and whether changes coincide with better visibility.
It involves reliable scheduled querying across several models, parsing messy free-text answers, storing large volumes of results, building dashboards and, for a commercial product, multi-tenant accounts and billing. Getting brand detection and sentiment accurate enough to trust takes careful testing.
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