DaVinci Commerce Announces Agentic BrandStore Enterprise: End-To-End Agentic Commerce From Discovery To Purchase Across AI platforms

Introduces AI-powered Content Enrichment Engine to add conversational context to product content, driving precise brand and product discoverability across LLM platforms with added support for GEO, ACP and UCP.

SAN MATEO, Calif., June 22, 2026 /PRNewswire/ -- DaVinci Commerce, the agentic commerce experience platform for global brands and retailers, today announced the general availability of Agentic BrandStore™ Enterprise, which extends consumer experience to the entire journey: discovery, experience, and purchase on AI platforms.

The announcement comes as AI platforms cross from novelty into commerce infrastructure. ChatGPT processes 50 million shopping-intent queries every day and has reached 900 million weekly active users, a number that doubled in twelve months. Traffic from AI sources to U.S. retail sites grew 393% year over year in Q1 2026, according to Adobe Analytics, and that traffic converts 42% better than non-AI traffic. The change is real and growing, but many brands remain almost entirely undiscoverable.

The Discovery Problem: Product content was written for search engines. Consumers have conversations with LLMs.

When a consumer asks ChatGPT, "I'm going to a friend's wedding this weekend and it might get a little hot, what should I wear?" they are not searching for a product by name. They are describing a need. Most product content is structured for search, not conversations, optimized for keyword search, and is not equipped to answer that question. Ingredients, technical specs, and product names do not map directly to how consumers converse with AI agents. If the product content doesn't match how the question is asked, the brand does not get found.

Brands that submit existing product data directly to ACP or UCP feeds or use only that data to enrich PDP content on their sites, are finding it falls short. Product listings describe what something is. They don't capture how it's actually used, what customers say about it, or the conversational context that determines whether an LLM surfaces it in response to a real consumer query. That missing context is why enrichment from the product feed alone doesn't work.

"LLMs are where many consumers start their shopping journeys," said Diaz Nesamoney, Founder & CEO of DaVinci Commerce." Consumers are on these platforms asking questions about their needs, and brands need to be there from discovery to purchase. To be discoverable requires contextually enriched product content: ACP/UCP/GEO to get you found, and a branded storefront experience to get you chosen. All these together can make first-movers the category leader on these platforms."

Discovery: DaVinci Commerce Content Enrichment Engine transforms raw product data to conversation-ready metadata

The Content Enrichment Engine is a core component of DaVinci Commerce's platform. It takes a brand's existing product catalog and enriches every SKU with contextual intelligence drawn from multiple sources simultaneously using a scalable swarm of content agents: verified customer reviews, social conversations from Reddit and YouTube, consumer intent signals from GEO platforms, lifestyle content from brand websites, and real-time data on what questions consumers are actually asking LLMs. The engine uses a RAG (etrieval-augmented generation) and context engineering architecture to keep answers accurate, current, and calibrated to specific consumer queries.

This enriched content has two distinct outputs, which serve two fundamentally different functions:

• Discovery layer (ACP+, UCP+, GEO+): Enriched product descriptions are submitted to LLM platforms via enriched ACP and UCP feeds, and published back to retailer PDPs so crawlers pick up updated, context-rich content for GEO. The "+" in each case signals enrichment — a product feed with added intelligence matched to how consumers actually ask questions.
• Experience layer (the Storefront): The same enriched content, all user reviews, all social signals, all lifestyle contexts are also vectorized and stored for easy retrieval and powers the Answer Agent inside the Agentic Storefront. Unlike feed submissions, which are constrained by field limits and character caps, the Storefront has access to the full contextual dataset. CRM data, personalization context, and proprietary brand content never leave DaVinci's platform. Only answers are shared with the LLM, not the underlying data.

The practical implication of this architecture is significant: through the discovery feeds, DaVinci gives an LLM enough context to surface a brand's products via their product discovery tools. Through the Storefront's Answer Agent, DaVinci can answer virtually any question a consumer asks, matching it accurately to products because the full enriched dataset is available, in real time, without the constraints that govern feed submissions.

Experience: Significant Updates to make Storefront Enterprise-Ready

• Support for Multiple LLMs and Chat Interfaces: With the addition of ACP and UCP support, BrandStore experiences can now be made available to LLM platforms like Gemini, Claude and can eventually be integrated into Retailer Agents like Alexa and Sparky when they support ingestion of enriched product content via UCP. In addition, BrandStore now supports Website Chat integration via an MCP server that can bring all the powerful Agentic BrandStore experiences and capabilities to a retailer or brand owned website or mobile app. This will also enable easy integration with emerging chat interfaces like WhatsApp and Instagram.
  
  
• Compliance Checking: Agentic BrandStore now has enhanced compliance checking whereby a brand or retailer can upload a set of compliance guidelines and rules that will be automatically checked by the Answer Agent to ensure non-compliant content does not get published to LLMs or consumers. This is a critical guardrail to ensure enterprise-grade compliance.
  
  
• Multi-Agent Support for Content: Integrating enterprise content is not easy as enterprise product content often sits in many different enterprise platforms. For example, reviews may be in a reviews platform. Product content is in one or more PIMs or a feed management system. Consumer data is in a CRM system. This new capability enables multiple content agents to collaborate, fetch, and integrate product and lifestyle content into the Answer Agent.
  
  
• Ratings and Reviews: BrandStore now supports a new ratings and reviews tool where consumers can establish trust before purchasing a product. Research shows that social proof via user ratings and reviews is critical to establishing trust with new-to-brand consumers. The ratings and reviews tool allows consumers to browse other consumers' product feedback before making a purchase decision.

Platform coverage
Through a single integration, the DaVinci Agentic BrandStore platform supports the full range of AI commerce surfaces: ChatGPT (ACP+ discovery + Storefront app), Google Gemini (UCP+ discovery), brand-owned website AI agents, and retailer PDPs via GEO+ enrichment. Enterprise integrations connect to PIM systems, DAM platforms, Ratings and reviews platforms, CRM systems, and order and fulfillment platforms.

Availability
The DaVinci Agentic BrandStore platform is available today. The no-code BrandStore Studio allows brands to configure and launch a storefront in approximately two to four weeks, with OpenAI approval typically ranging from two days to several weeks. Three tiers are available: a base package covering ACP+, UCP+, and GEO+; a Standard version adding the full Storefront experience layer; and an Enterprise version with custom tooling, multilingual stores (15–30+ languages), and advanced analytics.

About DaVinci Commerce
DaVinci Commerce is the agentic commerce experience platform for global brands and retailers purpose-built for the AI commerce era, where consumers discover and buy through conversational interfaces rather than search and click.

The Content Enrichment Engine transforms a brand's existing product catalog into AI-ready intelligence by drawing from Bazaarvoice reviews, social signals, and real-time consumer intent data. It powers two outputs: enriched discovery feeds via ACP+, UCP+, and GEO+; and the Answer Agent driving the branded Agentic Storefront inside ChatGPT and brand-owned surfaces where proprietary brand data never leaves DaVinci's platform.

Live in approximately two to four weeks via a no-code Studio, this is a single platform providing full purchase journey coverage.

Founded by Diaz Nesamoney, founder of Informatica, and backed by Accenture, DaVinci Commerce is trusted by Nestlé, Diageo, Giant Eagle, Nordstrom, and many more. We deliver speed, scale, precision personalization, and measurable growth.

Learn more at www.davincicommerce.ai.

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