Kiliagon Agentic Radar Launches to Dominate the Age of Agentic Commerce on Amazon

The world of Retail Media is undergoing an unprecedented revolution. The introduction of generative AI assistants, such as Rufus in Europe and Alexa for Shopping in the United States, is radically redefining the user purchase journey.

Today, Amazon's algorithm no longer simply ranks products based on keyword accumulation. AI explains products, compares them and answers complex consumer questions. In this new scenario, traditional SEO is officially outdated. Leaving a brand's reputation in the hands of an algorithm without a structured strategy is a risk brands can no longer afford.

What Agentic Commerce Is and the GEO Revolution

To respond to this transformation, Kiliagon announces the launch of Kiliagon Agentic Radar: a proprietary GEO, Generative Engine Optimization, methodology on Amazon, supported by advanced analytics technology.

This pioneering approach was designed to guide brands through the new Agentic Commerce paradigm, turning Amazon's Artificial Intelligence into an active product promoter. The arrival of GEO on Amazon marks a decisive turning point for retail media: continuing to invest in the old logic of traditional SEO means playing a game that no longer exists.

We are now officially in the age of Agentic Commerce, a radical paradigm shift that is changing how consumers interact with brands and products. This evolution requires brands to rewrite both the KPIs they monitor and the related organic and advertising strategies from the ground up.

The Numbers Behind the Conversational Revolution

Persisting with old indexing strategies exposes brands to massive and unavoidable inefficiencies. Market data highlights the urgency of this change:

• The new search standard. 20% of Amazon searches already happen through conversational interfaces.
• The impact of Rufus. With more than 300 million users, consumers who use the AI assistant show 60% higher purchase propensity.
• Record conversions. During seasonal peaks, conversion rates can be up to 3.5 times higher than traditional searches.
• The voice boom. With the arrival of Alexa for Shopping, the search bar becomes a fully conversational interface, mirroring the trend behind Google AI Overviews. Its future arrival in Europe will further accelerate the shift, supported by advanced voice-assistant purchases that have already tripled.

The Methodology: The 5 Pillars of Kiliagon Agentic Radar

The scientific protocol behind Kiliagon Agentic Radar combines semantic intelligence and data governance strategies to ensure AI actively recommends your brand as the leading authority in its category.

The methodology is built around five key actions:

• Semantic Intelligence. Semantic mapping of category Golden Prompts used by shoppers in their searches.
• SOAR Audit. Competitive monitoring of brand visibility through the proprietary Weighted SOAR metric.
• Semantic Alignment. Content optimization to be accurately recommended by Amazon's algorithm.
• Data Governance. Catalog data engineering to eliminate AI interpretation errors.
• Gap Bridging. Analysis of residual gaps to protect and consolidate generative market share.

The Proprietary Dashboard

Alongside the strategic methodology, which can gather insights from all major LLMs, the tool includes a proprietary dashboard. Because the AI assistant responds in natural language and suggests alternatives to consumers, appearing inside those answers is now the new position on the digital shelf.

The interactive Kiliagon Agentic Radar interface captures the effectiveness of this positioning by measuring:

• The level and quality of Rufus recommendations within a specific category.
• The visibility and sentiment generated by the algorithm.
• The brand's strength across the customer's full decision funnel.
• Advanced metrics that can be compared with competitor brands in real time and monitored over time.

Are You Ready for Agentic Commerce?

Kiliagon is already analyzing the largest Amazon categories to map how AI agents consider brands. This makes it possible to understand in advance where to intervene and how to optimize commercial performance, with the strategic goal of giving partners the strongest possible position inside generative recommendations.