Deep and multi-LLM semantic analysis
Visibility analysis is not enough if you ignore the nature of what AI engines are saying. BotRank performs an exhaustive semantic analysis on every response collected daily across all AI engines: ChatGPT, Google AI Overview, Gemini, Perplexity, Mistral, Copilot, Grok, Claude....
Our technology aggregates the terms most used by LLMs to talk about your brand or products. Through dynamic word clouds and precise extraction of recurring themes, you can instantly visualize your company's semantic footprint in the AI ecosystem.
The result: a clear understanding of the vocabulary associated with your offering by the algorithms informing your future customers.
Our technology aggregates the terms most used by LLMs to talk about your brand or products. Through dynamic word clouds and precise extraction of recurring themes, you can instantly visualize your company's semantic footprint in the AI ecosystem.
The result: a clear understanding of the vocabulary associated with your offering by the algorithms informing your future customers.
Measure sentiment and identify sources
Knowing what AI engines say is essential, knowing whether it is positive or negative is strategic. The Perception module automatically qualifies the overall sentiment of your brand and breaks down discussion snippets by tone (positive, neutral, negative).
But BotRank goes further: the platform allows you to drill down precisely to the sources that negatively (or positively) influence AI engines. You no longer just observe an unfavorable opinion, you identify the exact article, forum, or website that fed the algorithm.
This unprecedented traceability gives you the keys to understanding the recommendation mechanics of LLMs.
But BotRank goes further: the platform allows you to drill down precisely to the sources that negatively (or positively) influence AI engines. You no longer just observe an unfavorable opinion, you identify the exact article, forum, or website that fed the algorithm.
This unprecedented traceability gives you the keys to understanding the recommendation mechanics of LLMs.
From raw data to strategic recommendation
This perception data is not just a set of indicators: it constitutes a strong strategic lever to guide your content production.
By identifying perceived weaknesses or negative influential sources, you can produce the right content to counterbalance the narrative and correct the course. These insights directly feed the BotRank Agent, which generates highly targeted optimization recommendations.
You thus transform a passive perception analysis into a proactive action plan, ensuring that AI engines always present the best version of your brand.
By identifying perceived weaknesses or negative influential sources, you can produce the right content to counterbalance the narrative and correct the course. These insights directly feed the BotRank Agent, which generates highly targeted optimization recommendations.
You thus transform a passive perception analysis into a proactive action plan, ensuring that AI engines always present the best version of your brand.
