New AI Optimization Agent Helps Brands Improve SKU Visibility and Product Recommendations Across ChatGPT, Claude, Gemini, Perplexity, and Grok
Emberos, a company focused on helping brands manage and improve their visibility within artificial intelligence-driven search and shopping ecosystems, has officially launched Merchant, a new AI-powered optimization agent designed to give businesses SKU-level insight and control across major AI shopping environments. The company describes Merchant as the first platform built specifically to not only monitor how products appear in AI-generated shopping recommendations, but also actively improve and optimize those results across leading large language model ecosystems.
The launch represents another major development in the rapidly evolving field of AI-driven commerce, where conversational AI platforms such as ChatGPT, Claude, Gemini, Perplexity, and Grok are increasingly influencing how consumers discover, compare, and purchase products online.
Merchant becomes the fourth agent within the broader Emberos AI Visibility platform, joining Scout, Pilot, and Flow as part of the company’s expanding ecosystem of AI optimization tools. Together, these products aim to provide enterprises with monitoring, predictive analysis, workflow automation, and optimization capabilities specifically tailored for the age of AI-mediated commerce and conversational search.
Founder and Chief Executive Officer Justin Inman said the emergence of AI shopping assistants has fundamentally changed how products are surfaced and recommended to consumers.
According to Inman, every product SKU now effectively has a “second storefront” inside AI systems — one that brands neither created nor fully control. He argued that while many businesses have spent the past year and a half trying to measure whether their products appear inside AI-generated shopping recommendations, measurement alone is no longer enough.
Inman stated that brands do not simply need dashboards showing where they are losing visibility inside AI shopping experiences. Instead, they require systems capable of actively improving recommendation outcomes and generating measurable commercial impact. Merchant, he said, was built specifically to address that need.
The timing of the launch reflects the growing importance of AI agents in shaping global consumer purchasing behavior. Research cited by Emberos from the Interactive Advertising Bureau indicates that nearly 40% of consumers already use AI tools during purchasing decisions, while 80% expect their use of AI-assisted shopping experiences to increase further in the coming years.
Additional research from VML, cited through eMarketer, suggests that consumer behavior shifts that previously unfolded over a decade during the rise of traditional ecommerce are now occurring within as little as 12 to 24 months due to the rapid adoption of generative AI technologies.
Meanwhile, projections from McKinsey & Company estimate that the global market opportunity for agentic commerce — where AI systems actively guide or automate buying decisions — could reach between $3 trillion and $5 trillion by 2030.
These trends are creating significant pressure on brands to understand how their products are represented inside AI-generated shopping conversations. Unlike traditional ecommerce websites or search engines, conversational AI systems do not simply display product listings in response to keyword searches. Instead, they synthesize recommendations using large volumes of structured and unstructured information pulled from across the internet.
AI shopping agents evaluate data from retailer catalogs, product specifications, online reviews, editorial content, social discussions, third-party databases, and broader web context when generating responses for users.
As a result, brands can no longer rely solely on conventional search engine optimization or marketplace advertising strategies to maintain visibility. If a product’s information is incomplete, inconsistent, poorly structured, or insufficiently represented within the broader AI ecosystem, it may fail to appear in AI-generated purchase recommendations altogether.
Emberos argues that this shift creates a new competitive battleground centered around AI visibility optimization.
Merchant is designed to help companies address this challenge by ingesting an organization’s entire product catalog and continuously tracking how each SKU appears inside AI-driven shopping conversations. The system analyzes product-level visibility across multiple large language model environments and evaluates how AI systems surface products under different consumer intent scenarios.
The platform tracks what Emberos calls Product Recommendation Rate, or PRR, a proprietary metric designed to measure how frequently a product SKU appears in AI-generated shopping responses. Merchant segments performance data according to prompt intent categories, including informational queries, comparative product research prompts, and transactional purchasing requests.
This allows brands to understand not only whether products appear inside AI shopping environments, but also how they perform during different stages of the customer decision-making journey.
Merchant supports visibility analysis across several leading AI ecosystems, including ChatGPT, Gemini, Claude, Perplexity, and Grok. This cross-platform capability is becoming increasingly important as the AI assistant landscape grows more fragmented and consumers adopt different AI tools for different use cases.
One of Merchant’s most significant differentiators, according to Emberos, is its ability to move beyond passive analytics and into active optimization.
When the platform identifies visibility gaps or underperforming SKUs, Merchant automatically deploys what the company calls “Fix Packs.” These are automated optimization workflows designed to implement recommended changes across enterprise operational systems.
The workflows integrate directly with widely used enterprise platforms such as HubSpot, Slack, and Jira, enabling marketing, ecommerce, product, and operations teams to execute AI visibility improvements more efficiently.
According to Emberos, the system then re-measures product visibility performance after optimization changes are implemented, allowing brands to validate whether improvements generated measurable gains in recommendation frequency and commercial outcomes.
Internal validation data shared by the company suggests Merchant has delivered an average 8% improvement in Product Recommendation Rate per SKU, along with measurable downstream revenue impact associated with increased AI-generated visibility.
Inman emphasized that even relatively small increases in what he described as “Share-of-Prompt” can generate meaningful business value as conversational AI becomes a larger driver of consumer purchasing behavior.
He argued that while measurement tools provide insight into performance trends, businesses ultimately need optimization systems capable of influencing outcomes directly. According to Inman, Emberos is positioning itself not merely as a data provider, but as a platform focused on measurable commercial impact.
Merchant operates on top of the broader Emberos Brand Knowledge Graph, a proprietary data architecture that connects prompts, AI-generated responses, competitor activity, customer interactions, and commercial outcomes across paid, owned, and earned digital channels.
This shared infrastructure supports all four products within the Emberos platform ecosystem:
- Scout, which focuses on AI visibility monitoring
- Pilot, centered on predictive visibility intelligence
- Flow, which manages workflow orchestration and automation
- Merchant, which introduces direct commerce optimization functionality
The company believes this integrated architecture differentiates Emberos from point-solution competitors that provide analytics dashboards without deeper workflow automation or optimization capabilities.
According to Inman, traditional dashboards simply show businesses the current score, whereas Emberos products are designed to actively improve performance inside AI ecosystems.
The rise of AI visibility optimization reflects broader changes taking place across digital commerce and online discovery. Historically, brands focused heavily on traditional search engine optimization, paid advertising, marketplace positioning, influencer marketing, and social media engagement to drive online visibility.
However, large language models and conversational AI assistants operate differently from traditional search engines. Rather than ranking pages primarily based on keywords and backlinks, AI systems synthesize contextual understanding from multiple sources simultaneously when generating recommendations.
This means that future ecommerce competitiveness may depend increasingly on how effectively brands structure and distribute product information across the broader AI-consumable web ecosystem.
Merchant launches with enterprise customer commitments spanning industries including apparel, consumer packaged goods, and entertainment. Emberos indicated that enterprise interest in AI commerce optimization has accelerated as brands begin recognizing the strategic importance of AI recommendation visibility.
One of Merchant’s early strategic partners is Stagwell, which has integrated the platform into Stagwell Search+, the company’s AI-powered search and visibility platform.
Through Stagwell’s omnichannel media agency Assembly, Merchant capabilities are expected to become available across a global network of more than 70 agencies serving enterprise clients worldwide.
Dan Roberts, Global Senior Vice President of Search at Assembly Global, said many competing AI visibility platforms currently provide insight without offering meaningful optimization capabilities tied directly to measurable business outcomes.
According to Roberts, Merchant introduces the actionable optimization layer many enterprise clients have been waiting for. He noted that integrating Merchant into the Search+ ecosystem is already producing measurable results for clients.
Industry analysts expect the market for AI commerce optimization, conversational search visibility, and AI recommendation management to expand significantly over the next several years as generative AI becomes more deeply integrated into consumer shopping experiences.
As conversational AI increasingly mediates product discovery and purchasing decisions, brands may need entirely new operational frameworks for managing visibility within AI-generated ecosystems.
With Merchant, Emberos is attempting to position itself at the forefront of this emerging market category by offering brands not only visibility into how AI systems recommend products, but also the ability to actively shape, optimize, and improve those recommendations across the rapidly growing landscape of AI-driven commerce.
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