- Agentic Discoverability Engine™ enriches product catalogs with consumer context and measures SKU-level discoverability
- Brand Agent™ publishes and distributes enriched, machine-readable product knowledge in machine-readable forms across LLMs such as ChatGPT, Gemini, Claude and personal AI agents such as Meta Muse, OpenAI dots and Instinct; and retailer-owned shopping agents such as Walmart's Sparky and Amazon's Alexa for Shopping
- Agentic BrandStore™ enables curated, conversational brand and retail experiences for consumers inside AI platforms.
Together, these capabilities create an enterprise commerce infrastructure layer between existing product systems and a new generation of AI-powered shopping experiences.
AI Agents Need More Than Traditional Product Catalogs
Consumers increasingly turn to LLMs and personal AI agents to research products, evaluate alternatives and make shopping decisions. Acting on consumers' behalf, these systems need reliable, accurate product information that connects individual products to the needs, preferences and problems consumers express in natural language.
Most product catalogs, however, were not built for this purpose. They primarily describe what a product is: i.e., its attributes, specifications, ingredients and features rather than the many reasons why, when and for whom that product may be relevant.
A shopper might ask, "What foundation won't turn orange or look cakey after a few hours?" Another might ask, "I have sensitive skin. Is there a foundation better suited for me?"
The answer to both questions could be the same product, even though neither need may appear explicitly in its traditional product description.
DaVinci Commerce's expanded Agentic Commerce Experience Platform is designed to bridge that gap by transforming conventional product catalogs into contextual, machine-readable product knowledge that AI agents can understand, retrieve and use.
Product Context Memory: Describe A Product In Thousands Of Ways
At the core of the platform is DaVinci Commerce's patent-pending Product Context Memory technology.
PCM maintains a growing body of contextual knowledge around each SKU, connecting authoritative product information with relevant benefits, consumer needs, use cases, occasions, preferences and supporting evidence.
Research agents continuously draw on product catalogs, ratings and reviews, social conversations, lifestyle content and other trusted sources to build and refresh this context. PCM organizes the resulting product knowledge into a dynamic knowledge graph and uses semantic indexing to make relevant context retrievable based on the meaning and intent of a consumer's question.
Rather than creating a single "optimized" description of a product, PCM enables the same SKU to be understood in potentially thousands of different contexts.
That context can also evolve with consumer behavior, seasonality and occasion. A sneaker, for example, might be relevant to students during back-to-school shopping, to runners based on its performance characteristics or as a gift during the holiday season. The underlying product remains the same; the context through which consumers discover it changes.
"Consumers describe their lives, their needs and the problems they want to solve to personal AI agents. Most product catalogs still speak in technical specifications," said Diaz Nesamoney, founder and CEO of DaVinci Commerce. "Product Context Memory connects those two worlds. Our Agentic Commerce Experience Platform gives brands and retailers the infrastructure to continually build that context, make it available to AI agents and measure whether it is actually helping their products get discovered and chosen. As AI agents become a new gateway to commerce, the quality and depth of product knowledge will increasingly determine which products earn a recommendation."
Three Integrated Components For Agentic Commerce
The expanded DaVinci Commerce Agentic Commerce Experience Platform consists of three integrated components spanning product knowledge, agent distribution and consumer experience that power the Agentic Discoverability Engine:
Agentic Discoverability Engine: Enrich, Contextualize And Measure
The Agentic Discoverability Engine transforms traditional product catalogs into rich contextual product knowledge designed for AI-powered discovery through three key integrated capabilities:
- Product Enrichment Engine uses research agents to identify and develop relevant consumer needs, benefits, use cases, occasions, preferences and supporting evidence for individual products.
- Product Context Memory maintains that knowledge over time, creating a persistent and evolving contextual layer around each SKU rather than relying on a single static product description.
- Product Discovery Intelligence (PDI) closes the loop by measuring discoverability at the brand, category and individual SKU level. Teams can establish baselines, identify gaps, compare raw and enriched product catalogs in controlled A/B tests, and track changes in retrieval, recommendations, mentions, relative rank, competitive share of voice and enriched-content adoption.
PDI also measures attributed discoverability: whether products appearing in AI-generated responses are associated with, and lead consumers back to authoritative brand or retailer product pages rather than simply being mentioned.
Together, enrichment, Product Context Memory and Product Discovery Insights create a continuous optimization cycle.
Brand Agent: Representing The Brand To Personal AI Agents
As consumers increasingly rely on AI agents to act on their behalf, brands and retailers need infrastructure capable of representing their products to those agents.
DaVinci Brand Agent provides that machine-facing layer.
Brand Agent takes the contextual product knowledge created by the Agentic Discoverability Engine and publishes and distributes it in the machine- and agent-readable formats increasingly used across agentic commerce.
These include structured catalog protocols such as ACP and UCP, Model Context Protocol (MCP) services, JSON-LD, structured product feeds, and enriched product content distributed back to retailer product detail pages through existing PIM and commerce infrastructure.
This enables brands and retailers to build on their existing product systems while making their catalogs more useful to personal AI agents, retailer-owned shopping agents, and other AI-powered shopping platforms.
The model creates a new relationship in commerce: personal AI agents interpret the shopper's needs and intent, while Brand Agent represents the brand and its products with authoritative, contextual product knowledge.
Agentic BrandStore: Bringing The Brand Experience To AI-Powered Shopping
While Brand Agent is designed primarily for machine-to-machine product discovery, Agentic BrandStore is designed for consumers who want to engage more deeply with a brand or retailer. This is especially important for the high-consideration categories many brands and retailers sell. A recommendation can begin the journey, but it rarely answers every question. People still want to understand tradeoffs, see alternatives and feel confident that the product fits their situation.
Agentic BrandStore creates curated, conversational shopping experiences inside AI platforms, allowing consumers to explore a brand's assortment, ask detailed questions, receive personalized product recommendations and move toward purchase within a brand-controlled experience.
This gives brands and retailers a consumer-facing complement to machine-level product distribution: Brand Agent helps products participate in AI-driven discovery and selection, while Agentic BrandStore provides a richer destination for consumers who want to engage directly with the brand.
Moving Beyond Citation-Focused GEO To Commerce-Ready Product Discovery
Much of the early conversation around generative engine optimization, or GEO, has focused on publishing articles, social posts and other human-readable content that LLMs may cite in their responses.
DaVinci Commerce believes that approach alone will be insufficient for agentic commerce.
As AI platforms increasingly ingest structured product catalogs and AI agents take greater responsibility for evaluating products, the requirements for commerce are different. Products need to be represented through accurate, authoritative and machine-readable data, but they also need sufficient contextual depth for an agent to understand why a particular product is relevant to a particular consumer need.
DaVinci Commerce brings consumer language and supporting evidence into the product knowledge itself. Ratings and reviews, social conversations, lifestyle content and other sources become inputs to structured enrichment while authoritative product facts remain at the center.
The result is not simply content designed to earn a citation. It is product knowledge designed to help LLMs and Personal AI Agents understand, retrieve, compare and recommend specific products.
Availability
The expanded DaVinci Commerce Agentic Commerce Experience Platform, including Brand Agent, the Agentic Discoverability Engine and patent-pending Product Context Memory technology, is available beginning October 1, 2026.
To learn more or request a demonstration, visit https://davincicommerce.ai/.
About DaVinci Commerce
DaVinci Commerce provides an Agentic Commerce Experience Platform™ that helps brands and retailers turn AI shopping conversations into commerce by getting their products discovered, helping shoppers choose with confidence and connecting those decisions to purchase.
The platform's Agentic Discoverability Engine™ enriches product catalogs with consumer context and measures discoverability at the SKU level. Brand Agent™ publishes and distributes enriched product knowledge to personal AI agents, retailer-owned shopping agents and commerce platforms. Agentic BrandStore™ turns product recommendations into guided, brand-controlled shopping experiences where consumers can explore, compare and move toward purchase, shortening the path to sales and helping brands and retailers drive measurable sales lift.
At the core of the platform is patent-pending Product Context Memory™, which maintains rich, evolving context around individual products so AI agents can better understand when, why and for whom each product is relevant.
Founded by Diaz Nesamoney, founder of Informatica, and backed by Accenture, DaVinci Commerce is trusted by Nestlé, Diageo, Giant Eagle, Nordstrom, and many more.
Learn more at davincicommerce.ai.
DaVinci Commerce, Agentic Commerce Experience Platform, Product Context Memory, Agentic Discoverability Engine, Brand Agent, and Agentic BrandStore are trademarks or pending trademarks of DaVinci Commerce, Inc. All other brands, products, or service names are the property of their respective owners.
SOURCE DaVinci Commerce
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