Adobe LLM Optimizer is a SaaS SEO optimization solution powered by artificial intelligence, designed to shape your brand presence in AI results and search assistants. It transforms AI exploration into qualified traffic and measurable influence throughout the customer journey.
In a landscape where AI models are constantly re-evaluating priorities, traditional SEO is showing its limits. Without a clear understanding of how your content is perceived by AI engines, visibility opportunities remain scattered and uncertain. LLM Optimizer centralizes analysis, proposes prescriptive adjustments, and measures the impact on credibility and traffic. By taking into account signals from AIs like GPT, Google AI Mode, and Perplexity, the tool helps you prioritize actions that actually change the volume and quality of traffic, and performance. The results are verified through indicators such as visibility, traffic and the quality of interactions.
Three to five key features that turn action into results: AI-ready content optimization for better relevance in the answers, deployment and governance through integrated workflows, AI exposure measurement and from voices, prescriptive recommendations for quick changes, and enterprise connectivity to integrate frictionlessly with your tools. These tools are based on clear metrics like conversion rate and time spent, and offer repeatable action plans for product and content teams.
What LLM Optimizer is not: it is not a simple traditional SEO audit tool, nor is it a standalone content generator. It does not replace your teams; it coordinates with them. It is an AI-oriented platform, designed to integrate into your ecosystem and support informed decisions through data and governance workflows. This also includes advice on the editorial calendar, rich formats, and alignment with customer experience priorities. The result is a clear roadmap: what needs to be optimized, when, and how.
In summary, Adobe LLM Optimizer offers a clear method for gaining AI visibility and qualified traffic, while measuring the impact on business goals. It is not a miracle promise, but a structured framework that transforms insight into measurable actions and sustainable value. This framework is based on AI-specific performance measures, and ensures robust governance through approval workflows and secure integrations.
MentionLab is an SEO software powered by AI. It helps teams understand and improve their visibility in the results and responses generated.
Today, SEO is guided by evolutionary algorithms and AI systems that define results. Teams struggle to track relevant mentions, prioritize opportunities, and measure the impact of actions. MentionLab centralizes analysis, automates monitoring and transforms data into actionable recommendations. This approach guarantees better traceability of actions and reduces duplication in SEO efforts. It also makes it possible to quickly demonstrate the impact of optimizations to management.
Fundamental, MentionLab integrates 4 key functionalities. Query analysis to identify areas with high potential and guide optimizations. Source mapping in order to target the influences that fuel AI results. Sentiment tracking that measures the opinion around your brand and informs reputation actions. Visibility and traceability a transparent crawler that explains how content is found and interpreted. At the same time, the platform offers clear data visualization, relevant alerts, and adaptable reports for teams and agencies.
To avoid confusion, MentionLab is not an automated writing platform, or an advertising solution, or an uncontrolled scraping tool. It does not replace human expertise but supports strategic decisions and alignment with your goals. This software doesn't promise results without action; it turns information into recommendations that your teams can validate. It's not just a data connector either: it integrates to support clear governance of SEO initiatives.
By bringing together data, insights and actions around AI, MentionLab offers a measurable approach to modern SEO. It helps brands and marketing teams gain clarity about opportunities, reduce uncertainty, and align their efforts with concrete results, without forced software commitments. Neutral testing can be useful to quickly assess the impact on your workflows. This approach makes it possible to communicate results more transparently and prepares for the evolution of internal practices towards useful and ethical AI. In practice, this means concise reports, relevant alerts, and gradual integration with teams, so everyone can act quickly without unnecessary complexity and with confidence.
