Quno is an SEO software powered by artificial intelligence, designed to sustainably optimize your visibility and guide your content strategy. It analyzes the research landscape and proposes priority actions to act with clarity and sustainable effectiveness.
Today's SEO requires a quick understanding of search intentions, competitors, and content that works. Without a consolidated view, teams spend time on vague optimizations and lose opportunities to align brand and performance. Quno provides central visibility and actionable recommendations to prioritize efforts and monitor results. Concretely, this results in clear action plans and a reduction in delays in implementing and measurable optimizations.
Quno brings together functionalities designed for AI and SEO, in order to transform data into decisions. Among them: SEO for AI Search Engines, which allows you to control how AI engines talk about your brand; Brand Tracking, to track fame and feeling over time; AI Interviewer, for large-scale qualitative interviews and actionable reports; Competitive Analysis, to measure your performance against your rivals; Citation Tracking, in order to identify the sources that fuel AI responses. Each feature brings Time saver, precision and strategic alignment between your brand and your content. These tools help you prioritize by impact, automate repetitive tasks, and generate clear reports for stakeholders.
Quno is not a human content management platform or a simple keyword tool. It's also not an advertising platform; it's a suite focused on understanding and controlling AI conversations around your brand. It clarifies the priorities between technical SEO, content and UX.
In summary, Quno offers a unified vision of your presence in AI engines and allows you to act with precision. By combining observation, analysis, and operational advice, it helps teams prioritize actions, monitor developments, and quickly adjust strategies. The potential lies in the ability to test hypotheses and measure the impact on brand visibility and health in a sustainable manner.
LLMRefs is an SEO software powered by artificial intelligence. It tracks, analyzes, and optimizes your visibility in AI-generated searches, including ChatGPT, Gemini, and Claude.
In a changing SEO landscape, authority signals and results generated by AI are no longer just links. LLMRefs centralizes the monitoring of your visibility in AI engines and explains why your content is cited or ignored by models like ChatGPT or Gemini. New practices are needed: decisions are based on real-time data and on the understanding of AI phenomena that influence rankings and user responses.
For example, an e-commerce publisher can track changes in positioning after the launch of a product and adjust category pages accordingly.
Key features include: real-time tracking rankings on the main AI models (ChatGPT, Google Gemini, Claude), exploration and monitoring of keywords thanks to the AI Keyword Tracker, quick competitive analysis to identify gaps and opportunities, automated reports that translate data into clear actions and targeting the sources cited to strengthen your credibility with AIs. You also have a consolidated dashboard, customizable alerts, and a ready-to-use CSV export for teams.
The platform also facilitates collaboration: shared views, comments, and quick exchanges between marketing and content teams.
LLMRefs is not a simple traditional SEO citation tool or an AI editing platform that generates content without a framework. It is an insights and optimization platform based on AI signals and model data to guide marketing and SEO decisions. It does not impose unique workflows, it adapts to your ecosystem and your goals, whether you are an online store, agency or large account.
Also note that LLMRefs does not assume human expertise or a global content strategy; it complements the analyses and actions planned by your team.
By making visible what AI engines use to assess your content, LLMRefs helps align your strategy with current AI requirements. The software helps marketing managers prioritize actions, measure impact, and iterate quickly without unnecessary complexity. The aim is to provide operational clarity and better visibility into real performance in AI environments.
This alignment with AI requirements can help prioritize tests and demonstrate the impact of optimizations on results.
