New AI Commerce Platform Helps Brands Track, Optimize, and Improve SKU Visibility Across ChatGPT, Gemini, Claude, and Other AI Shopping Ecosystems
Emberos, a company focused on AI visibility and optimization technologies for brands, has launched Merchant, a new AI-powered platform designed to help businesses monitor and improve how their products appear inside conversational AI shopping environments such as ChatGPT, Claude, Gemini, Perplexity, and Grok. The company describes Merchant as the first AI visibility optimization layer built specifically for commerce, giving brands the ability not only to track product representation inside AI-generated shopping recommendations but also to actively optimize and improve it.
The launch reflects the rapidly evolving role of generative AI in consumer purchasing behavior and digital commerce. As conversational AI systems increasingly become part of how consumers research, compare, and select products online, brands are facing a new competitive landscape where visibility inside AI-generated responses may become as important as traditional search engine rankings or ecommerce marketplace placement.
Merchant becomes the fourth product within the broader Emberos suite of AI visibility tools, joining Scout, Pilot, and Flow as part of the company’s growing AI Visibility Operating System platform. Together, these products are designed to help brands understand, predict, and influence how large language models represent products, companies, and commercial information across AI-driven consumer interactions.
Founder and Chief Executive Officer Justin Inman said the launch addresses a major emerging challenge facing modern brands: the rise of what he describes as a “second storefront” inside AI systems that companies neither built nor fully control.
According to Inman, many brands have spent the past eighteen months focusing primarily on measuring whether their products appear in AI-generated shopping responses. However, he argues that measurement alone is no longer sufficient as AI increasingly influences real purchasing decisions. Instead, companies now require systems capable of actively improving product visibility and proving measurable commercial impact.
The launch comes amid broader industry recognition that generative AI is rapidly reshaping online commerce and consumer discovery behaviors. Research cited by Emberos from the Interactive Advertising Bureau indicates that nearly 40% of consumers already use AI tools when making buying decisions, while 80% expect their reliance on AI-assisted shopping experiences to increase in the future.
Additional research referenced from VML and eMarketer suggests that consumer behavior changes that took more than a decade during the original rise of ecommerce are now occurring within just 12 to 24 months as AI-driven discovery accelerates adoption patterns.
Meanwhile, consulting firm McKinsey & Company has projected that the global market opportunity for agentic commerce — where AI systems increasingly guide or automate purchasing decisions — could grow to between $3 trillion and $5 trillion by 2030.
These projections underscore why brands are becoming increasingly concerned about how AI systems interpret, prioritize, and recommend products during conversational shopping experiences.
Unlike traditional ecommerce environments where brands can directly manage product listings, advertising placements, and merchandising strategies, AI-driven recommendation systems operate differently. Large language models generate shopping recommendations by synthesizing information from structured product data, retailer catalogs, online reviews, editorial content, third-party sources, public web data, and broader internet context.
As a result, a brand’s visibility inside AI shopping environments may depend not only on its own product information but also on how external data sources describe and contextualize those products across the web.
Emberos argues that this creates an entirely new optimization challenge for brands. If a product’s information is incomplete, inconsistent, poorly structured, or underrepresented within the broader AI ecosystem, it may fail to appear in AI-generated shopping recommendations even if it performs well in traditional ecommerce channels.
Merchant is designed to address this problem by ingesting a company’s full product catalog and continuously analyzing how individual SKUs appear across multiple AI shopping environments. The platform tracks visibility and recommendation performance at the product level, giving brands detailed insight into how AI systems perceive and surface their offerings.
One of Merchant’s core metrics is Product Recommendation Rate, or PRR, which Emberos describes as a proprietary measurement tracking how frequently a SKU appears inside AI-generated shopping responses. The system analyzes performance across different prompt categories, including informational queries, comparative shopping requests, and transactional purchase-oriented prompts.
Merchant also monitors SKU visibility across multiple leading large language model ecosystems, including ChatGPT, Gemini, Claude, Perplexity, and Grok. This multi-platform approach reflects the increasingly fragmented nature of the AI assistant market, where consumers may rely on different AI tools depending on context, device ecosystem, or personal preference.
The company says Merchant moves beyond simple analytics dashboards by introducing automated optimization capabilities intended to improve AI recommendation performance directly.
When the system identifies visibility gaps or underperforming SKUs, Merchant deploys what Emberos calls “Fix Packs,” automated optimization workflows designed to implement corrective actions across enterprise systems. These workflows can push recommendations directly into platforms such as HubSpot, Slack, and Jira to streamline operational execution across marketing, ecommerce, content, and product management teams.
According to Emberos, the system then remeasures SKU performance after changes are implemented to verify whether optimization efforts successfully improved AI visibility outcomes.
Internal company validation cited by Emberos suggests that Merchant has produced an average 8% improvement in Product Recommendation Rate per SKU, with measurable downstream revenue impact tied to improved AI recommendation visibility.
Inman emphasized that even small increases in AI-driven product visibility can translate into meaningful commercial outcomes as conversational AI becomes a larger component of consumer purchasing behavior. He argued that measurement alone represents only the starting point of AI commerce optimization, while actual business value comes from the ability to improve outcomes and influence purchasing conversations directly.
Merchant is built on top of the broader Emberos Brand Knowledge Graph, a proprietary data architecture designed to connect prompts, responses, competitor activity, customer interactions, and commercial outcomes across paid, owned, and earned digital surfaces.
This shared infrastructure powers all four Emberos platform components:
- Scout, focused on monitoring AI visibility and presence
- Pilot, centered on predictive AI visibility analysis
- Flow, which manages workflow orchestration
- Merchant, which adds direct commerce optimization capabilities
The company believes this integrated architecture differentiates Emberos from point solutions that provide analytics without operational execution capabilities. According to Inman, traditional dashboards may explain performance trends, but the Emberos platform is designed to actively change outcomes rather than simply report them.
The emergence of AI visibility optimization as a new enterprise software category reflects broader shifts occurring across digital marketing, ecommerce, and search industries. As generative AI assistants increasingly mediate online discovery experiences, brands are beginning to recognize that optimization strategies developed for traditional search engines may not fully translate into AI-driven environments.
Historically, companies focused heavily on search engine optimization (SEO), marketplace ranking strategies, paid advertising, and social media engagement to improve online visibility. However, AI-generated recommendation systems synthesize information differently, often relying on broader contextual understanding rather than keyword matching alone.
This has created growing demand for tools capable of helping brands understand how AI systems interpret their products and what factors influence recommendation inclusion inside conversational shopping experiences.
Merchant is launching with enterprise commitments spanning industries including apparel, consumer packaged goods, and entertainment. Emberos said the platform has already attracted interest from brands seeking better visibility into how AI-driven shopping environments may affect future customer acquisition and product discovery.
One notable early partner is Stagwell, which has integrated Merchant into Stagwell Search+, the company’s AI-powered search platform. Through Stagwell’s omnichannel media agency Assembly, the technology is expected to become available across a global network of more than 70 agencies.
Dan Roberts, Global Senior Vice President of Search at Assembly Global, said most competing AI visibility tools currently provide only data and insights without offering meaningful operational optimization capabilities tied directly to measurable outcomes.
According to Roberts, Merchant changes that dynamic by introducing an actionable optimization layer capable of producing tangible results for enterprise clients. He noted that integrating Merchant into the Search+ platform is already generating measurable performance improvements.
The rise of AI commerce optimization platforms like Merchant highlights how rapidly generative AI is beginning to reshape digital commerce infrastructure. As consumers increasingly rely on conversational AI systems for product discovery and purchasing guidance, brands may need entirely new categories of tools and strategies to remain visible and competitive within AI-mediated shopping ecosystems.
Industry observers expect the market for AI visibility management, conversational commerce optimization, and AI recommendation analytics to expand significantly over the coming years as enterprises adapt to evolving consumer behaviors and AI-powered digital experiences.
With Merchant, Emberos is positioning itself at the forefront of this emerging category by offering brands not only visibility into how AI systems recommend products, but also the ability to actively shape and optimize those recommendations across the rapidly growing landscape of AI-driven commerce.
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