Agentic Commerce Glossary
Core Concepts
Agentic Commerce
Agentic commerce is the full shopping journey (discovery, exploration and purchase) carried out with AI, either in conversation with a shopper or by an AI agent acting on the shopper's behalf, on platforms such as ChatGPT, Gemini, Claude and Meta Muse. It usually starts with a shopper describing a need rather than naming a product. Brands now market to two audiences: shoppers and their agents.
Agentic commerce is the full shopping journey (discovery, exploration and purchase) carried out with AI, either in conversation with a shopper or by an AI agent acting on the shopper's behalf, on platforms such as ChatGPT, Gemini, Claude and Meta Muse. It usually starts with a shopper describing a need rather than naming a product. Brands now market to two audiences: shoppers and their agents.
Agentic Shopping
Agentic shopping is another term for agentic commerce. It describes the full customer journey (discovery, exploration and purchase) that takes place inside AI platforms, including Gemini, ChatGPT and Claude.
Agentic shopping is another term for agentic commerce. It describes the full customer journey (discovery, exploration and purchase) that takes place inside AI platforms, including Gemini, ChatGPT and Claude.
Conversational Commerce
Conversational commerce is buying and selling through natural-language dialogue instead of search-and-click navigation. On AI platforms, shoppers ask questions, compare options and get recommendations in one conversation, rather than filtering through pages of product listings.
Conversational commerce is buying and selling through natural-language dialogue instead of search-and-click navigation. On AI platforms, shoppers ask questions, compare options and get recommendations in one conversation, rather than filtering through pages of product listings.
Discovery → Experience → Purchase
Discovery → Experience → Purchase is the three-stage consumer journey in agentic commerce. In discovery, a product surfaces in an AI answer, typically in response to a stated need or use case. In experience, the shopper explores products in a branded conversation with reviews, variant details and lifestyle content; this is where products are chosen. In purchase, the shopper buys on the brand's site, a retailer's site or in a nearby store, or through agentic checkout where AI platforms support it.
Discovery → Experience → Purchase is the three-stage consumer journey in agentic commerce. In discovery, a product surfaces in an AI answer, typically in response to a stated need or use case. In experience, the shopper explores products in a branded conversation with reviews, variant details and lifestyle content; this is where products are chosen. In purchase, the shopper buys on the brand's site, a retailer's site or in a nearby store, or through agentic checkout where AI platforms support it.
Discovered, Chosen, Bought
Discovered, chosen, bought describes positive outcomes for a brand and their products in agentic commerce. A product must first appear in AI answers (discovered), then win the shopper's preference over alternatives (chosen), then convert through a clear path to purchase (bought).
Discovered, chosen, bought describes positive outcomes for a brand and their products in agentic commerce. A product must first appear in AI answers (discovered), then win the shopper's preference over alternatives (chosen), then convert through a clear path to purchase (bought).
AI Surface
An AI surface is any interface where shoppers interact with AI to research or buy products. Examples include ChatGPT, Gemini, Claude, Meta Muse, Perplexity and AI assistants on brand or retailer websites.
An AI surface is any interface where shoppers interact with AI to research or buy products. Examples include ChatGPT, Gemini, Claude, Meta Muse, Perplexity and AI assistants on brand or retailer websites.
LLM (Large Language Model)
An LLM is the AI model behind platforms like ChatGPT, Gemini and Claude. In commerce, LLMs increasingly act as shopping advisors that interpret a shopper's needs and recommend specific products.
An LLM is the AI model behind platforms like ChatGPT, Gemini and Claude. In commerce, LLMs increasingly act as shopping advisors that interpret a shopper's needs and recommend specific products.
AI Shopping Agent
An AI shopping agent is a general-purpose AI assistant, such as ChatGPT, Gemini, Claude or Meta Muse, that researches, compares and can buy products on a shopper's behalf across every brand and retailer. It is also called a consumer agent or horizontal agent. To recommend a brand's products, it needs an enriched, machine-readable product catalog. For example, given "Find me a foundation for acne-prone, sensitive skin under $50", it understands the need, finds products across brands and retailers, compares them, recommends one and can complete the purchase.
An AI shopping agent is a general-purpose AI assistant, such as ChatGPT, Gemini, Claude or Meta Muse, that researches, compares and can buy products on a shopper's behalf across every brand and retailer. It is also called a consumer agent or horizontal agent. To recommend a brand's products, it needs an enriched, machine-readable product catalog. For example, given "Find me a foundation for acne-prone, sensitive skin under $50", it understands the need, finds products across brands and retailers, compares them, recommends one and can complete the purchase.
AI-Referred Traffic
AI-referred traffic is website visits that come from links inside AI platforms. It is a fast-growing share of retail traffic and typically converts better than traditional search traffic, because shoppers arrive having already narrowed their choice.
AI-referred traffic is website visits that come from links inside AI platforms. It is a fast-growing share of retail traffic and typically converts better than traditional search traffic, because shoppers arrive having already narrowed their choice.
Agent-to-Agent Commerce
Agent-to-agent commerce is when a shopper's AI agent deals directly with a brand's agent to find, compare and buy products. The shopping agent asks; the brand's agent answers with structured, authoritative product data.
Agent-to-agent commerce is when a shopper's AI agent deals directly with a brand's agent to find, compare and buy products. The shopping agent asks; the brand's agent answers with structured, authoritative product data.
Discoverability & Optimization
Agentic Discoverability
Agentic discoverability is how often, and how prominently, a product is recommended by AI platforms and AI shopping agents in response to shopper prompts. What matters most for revenue is attributed discoverability: whether the product appears with a direct link to buy.
Agentic discoverability is how often, and how prominently, a product is recommended by AI platforms and AI shopping agents in response to shopper prompts. What matters most for revenue is attributed discoverability: whether the product appears with a direct link to buy.
Attributed Discoverability
Attributed discoverability tracks whether a product surfaces in an AI answer with a link back to the brand's or retailer's product page, the step that leads to a sale. It measures commerce impact, not just mentions.
Attributed discoverability tracks whether a product surfaces in an AI answer with a link back to the brand's or retailer's product page, the step that leads to a sale. It measures commerce impact, not just mentions.
Agentic Commerce Optimization (ACO)
Agentic commerce optimization is the practice of getting products discovered, chosen and bought by AI agents: measuring attributed discoverability, enriching product catalogs with shopper context and delivering them in machine-readable formats. Unlike GEO, it focuses on products and purchase, not citations.
Agentic commerce optimization is the practice of getting products discovered, chosen and bought by AI agents: measuring attributed discoverability, enriching product catalogs with shopper context and delivering them in machine-readable formats. Unlike GEO, it focuses on products and purchase, not citations.
GEO (Generative Engine Optimization)
GEO is the practice of making content easy for AI platforms to crawl, index and cite in generated answers. It is the AI-era counterpart to SEO and helps build brand awareness. For commerce it has limits: it rarely gets specific products recommended across the full range of shopper needs, or with a direct link to buy. That takes enriched, machine-readable product catalogs.
GEO is the practice of making content easy for AI platforms to crawl, index and cite in generated answers. It is the AI-era counterpart to SEO and helps build brand awareness. For commerce it has limits: it rarely gets specific products recommended across the full range of shopper needs, or with a direct link to buy. That takes enriched, machine-readable product catalogs.
Shopper Prompt
A shopper prompt is the question or request a consumer types into an AI platform before buying. Prompts usually describe an occasion, problem or goal, such as "a foundation that won't look cakey by afternoon," rather than a product name.
A shopper prompt is the question or request a consumer types into an AI platform before buying. Prompts usually describe an occasion, problem or goal, such as "a foundation that won't look cakey by afternoon," rather than a product name.
Need-Led Query
A need-led query is a shopper prompt that states a need, occasion or pain point instead of a product or brand. Traditional catalogs can't match these queries because product data describes specs, not use cases.
A need-led query is a shopper prompt that states a need, occasion or pain point instead of a product or brand. Traditional catalogs can't match these queries because product data describes specs, not use cases.
Discoverability Gap
A discoverability gap is a case where a product should be recommended for a relevant prompt but isn't, or appears without a purchase link. Gaps can be measured per SKU, per prompt and per LLM, then closed through contextual enrichment.
A discoverability gap is a case where a product should be recommended for a relevant prompt but isn't, or appears without a purchase link. Gaps can be measured per SKU, per prompt and per LLM, then closed through contextual enrichment.
Brand-Level vs. SKU-Level Visibility
Brand-level visibility, often reported as brand share of voice, measures how often a brand appears in AI answers overall, usually as one score. SKU-level visibility measures how often each product appears, and whether it appears with a link to buy. SKU-level measurement shows exactly which products are losing sales and what to fix.
Brand-level visibility, often reported as brand share of voice, measures how often a brand appears in AI answers overall, usually as one score. SKU-level visibility measures how often each product appears, and whether it appears with a link to buy. SKU-level measurement shows exactly which products are losing sales and what to fix.
AI Visibility Tool
An AI visibility tool monitors how a brand appears in LLM answers, typically reporting a brand-level score or share of voice on a dashboard. It tracks mentions, not whether specific products surface with a link to buy.
An AI visibility tool monitors how a brand appears in LLM answers, typically reporting a brand-level score or share of voice on a dashboard. It tracks mentions, not whether specific products surface with a link to buy.
LLM Citations
LLM citations are the sources an AI platform references or links to in its answers. Shifts in which sources are cited, such as reduced reliance on Reddit, directly affect which brands and products get surfaced.
LLM citations are the sources an AI platform references or links to in its answers. Shifts in which sources are cited, such as reduced reliance on Reddit, directly affect which brands and products get surfaced.
Zero-Click Experience
A zero-click experience is one where the shopper gets product answers, comparisons and recommendations inside the AI conversation without clicking through to a website. It replaces search-and-click browsing with dialogue.
A zero-click experience is one where the shopper gets product answers, comparisons and recommendations inside the AI conversation without clicking through to a website. It replaces search-and-click browsing with dialogue.
Product Data & Enrichment
PDP (Product Detail Page)
A PDP is the web page for a single product on a brand or retailer site, with its description, specs, images, price and reviews. PDPs are a key source of product information for AI platforms, especially when they carry structured data such as JSON-LD.
A PDP is the web page for a single product on a brand or retailer site, with its description, specs, images, price and reviews. PDPs are a key source of product information for AI platforms, especially when they carry structured data such as JSON-LD.
Product Feed
A product feed is a structured file listing products and their attributes, such as title, price, availability and specs. It is used to supply product data to retailers, ad platforms and, increasingly, AI platforms.
A product feed is a structured file listing products and their attributes, such as title, price, availability and specs. It is used to supply product data to retailers, ad platforms and, increasingly, AI platforms.
PIM / PXM
A PIM (Product Information Management) system is the central store for a company's product data. A PXM (Product Experience Management) system extends PIM to manage how products are presented across channels. Both are common starting points for enrichment.
A PIM (Product Information Management) system is the central store for a company's product data. A PXM (Product Experience Management) system extends PIM to manage how products are presented across channels. Both are common starting points for enrichment.
PFM (Product Feed Management)
PFM is the tool used to format, optimize and distribute product feeds to multiple channels, such as marketplaces, ad platforms and AI platforms, from one source.
PFM is the tool used to format, optimize and distribute product feeds to multiple channels, such as marketplaces, ad platforms and AI platforms, from one source.
Product Enrichment
Product enrichment is adding information to product data beyond basic specs. Most enrichment focuses on catalog and attribute completeness: filling in missing fields so a catalog can be ingested by AI platforms. How well it works depends on the sources used and on whether it adds context or only attributes. See contextual enrichment.
Product enrichment is adding information to product data beyond basic specs. Most enrichment focuses on catalog and attribute completeness: filling in missing fields so a catalog can be ingested by AI platforms. How well it works depends on the sources used and on whether it adds context or only attributes. See contextual enrichment.
Contextual Enrichment
Contextual enrichment is enriching each product with real-world context: use cases, occasions, pain points, lifestyle fit and customer language drawn from reviews, social content, lifestyle content and the open web. It describes each product in the thousands of ways shoppers ask, which attribute completeness alone can't do.
Contextual enrichment is enriching each product with real-world context: use cases, occasions, pain points, lifestyle fit and customer language drawn from reviews, social content, lifestyle content and the open web. It describes each product in the thousands of ways shoppers ask, which attribute completeness alone can't do.
Continuous Enrichment
Continuous enrichment is enrichment that runs always on, re-measuring and updating product context as shopper needs, seasons, competitors and AI models change. One-time enrichment goes stale within weeks.
Continuous enrichment is enrichment that runs always on, re-measuring and updating product context as shopper needs, seasons, competitors and AI models change. One-time enrichment goes stale within weeks.
Product Knowledge Graph
A product knowledge graph is a connected data structure that links each product to its attributes, use cases, occasions and pain points. It lets AI match a product to every need shoppers describe in prompts. DaVinci Commerce's Product Context Memory is a patent-pending, dynamic product knowledge graph built on a vector database with semantic indexing.
A product knowledge graph is a connected data structure that links each product to its attributes, use cases, occasions and pain points. It lets AI match a product to every need shoppers describe in prompts. DaVinci Commerce's Product Context Memory is a patent-pending, dynamic product knowledge graph built on a vector database with semantic indexing.
Machine-Readable Product Data
Machine-readable product data is product information structured so AI agents and crawlers can parse it reliably, rather than interpreting free-form web copy. Examples include ACP and UCP catalogs, JSON-LD and MCP services. AI shopping agents rely on it to compare and buy products.
Machine-readable product data is product information structured so AI agents and crawlers can parse it reliably, rather than interpreting free-form web copy. Examples include ACP and UCP catalogs, JSON-LD and MCP services. AI shopping agents rely on it to compare and buy products.
Research Agents
Research agents are DaVinci Commerce's AI agents that continuously gather, clean and match product context from reviews, ratings, social conversations, lifestyle content and the open web to each product. They make enrichment possible at catalog scale, without manual copywriting.
Research agents are DaVinci Commerce's AI agents that continuously gather, clean and match product context from reviews, ratings, social conversations, lifestyle content and the open web to each product. They make enrichment possible at catalog scale, without manual copywriting.
Protocols & Infrastructure
Machine-Readable Protocols / Formats ("Rails")
Machine-readable protocols and formats are the open standards that let AI platforms and agents read product data and process transactions. They include ACP, UCP, JSON-LD, MCP and AP2, and are often called the "rails" of agentic commerce. JSON-LD is technically a structured data format rather than a protocol, but it does the same job on product pages. The rails carry whatever product data a brand supplies; they don't create discovery or experience on their own.
Machine-readable protocols and formats are the open standards that let AI platforms and agents read product data and process transactions. They include ACP, UCP, JSON-LD, MCP and AP2, and are often called the "rails" of agentic commerce. JSON-LD is technically a structured data format rather than a protocol, but it does the same job on product pages. The rails carry whatever product data a brand supplies; they don't create discovery or experience on their own.
ACP (Agentic Commerce Protocol)
ACP is an open standard developed by OpenAI and Stripe that lets merchants share structured product data with ChatGPT and enables purchases within it. It is designed mainly for transactions and typically carries basic product specs.
ACP is an open standard developed by OpenAI and Stripe that lets merchants share structured product data with ChatGPT and enables purchases within it. It is designed mainly for transactions and typically carries basic product specs.
UCP (Universal Commerce Protocol)
UCP is an open standard led by Google and co-developed with Shopify and other retailers. It lets AI agents discover products, build carts and check out across merchants. Like ACP, it carries the product data a brand supplies; it doesn't add the context that gets a product chosen.
UCP is an open standard led by Google and co-developed with Shopify and other retailers. It lets AI agents discover products, build carts and check out across merchants. Like ACP, it carries the product data a brand supplies; it doesn't add the context that gets a product chosen.
JSON-LD
JSON-LD is a structured data format, typically used with the Schema.org vocabulary, that embeds machine-readable product information in a web page. Search engines and the AI assistants built on them use it to understand product details, offers and reviews. It should match the content shoppers see on the page.
JSON-LD is a structured data format, typically used with the Schema.org vocabulary, that embeds machine-readable product information in a web page. Search engines and the AI assistants built on them use it to understand product details, offers and reviews. It should match the content shoppers see on the page.
MCP (Model Context Protocol)
MCP is an open standard that lets AI models connect to external data sources and tools in real time. In commerce, a brand can expose an MCP service so AI platforms query current, approved product information directly instead of relying on crawled pages.
MCP is an open standard that lets AI models connect to external data sources and tools in real time. In commerce, a brand can expose an MCP service so AI platforms query current, approved product information directly instead of relying on crawled pages.
Brand MCP Service
A brand MCP service is an authenticated MCP endpoint that serves a brand's own product data and content to AI platforms. It gives agents a direct, brand-controlled source of product information instead of whatever they last crawled. It is one of the formats Brand Agent delivers.
A brand MCP service is an authenticated MCP endpoint that serves a brand's own product data and content to AI platforms. It gives agents a direct, brand-controlled source of product information instead of whatever they last crawled. It is one of the formats Brand Agent delivers.
AP2 (Agent Payments Protocol)
AP2 is an open protocol for securely authorizing payments made by or through AI agents. It is part of the payments layer of agentic commerce rails.
AP2 is an open protocol for securely authorizing payments made by or through AI agents. It is part of the payments layer of agentic commerce rails.
LLM Apps / ChatGPT Apps
LLM apps are brand- or retailer-built applications that run inside an AI platform, such as ChatGPT apps. They let a company provide curated product content, advice and purchase options within the conversation, and function as storefronts inside the LLM.
LLM apps are brand- or retailer-built applications that run inside an AI platform, such as ChatGPT apps. They let a company provide curated product content, advice and purchase options within the conversation, and function as storefronts inside the LLM.
Business Agents
Business agents are AI assistants that represent a brand or retailer and talk directly to shoppers, inside an AI platform or on the company's own site. Google's Business Agent is one example. Not to be confused with a Brand Agent, which talks to shoppers' AI agents.
Business agents are AI assistants that represent a brand or retailer and talk directly to shoppers, inside an AI platform or on the company's own site. Google's Business Agent is one example. Not to be confused with a Brand Agent, which talks to shoppers' AI agents.
Native LLM Storefronts
Native LLM storefronts are shopping experiences built by AI platforms or commerce providers themselves, for example by OpenAI, Google or Shopify. Each comes with its own protocols and metadata requirements, so supporting several means duplicating work per platform.
Native LLM storefronts are shopping experiences built by AI platforms or commerce providers themselves, for example by OpenAI, Google or Shopify. Each comes with its own protocols and metadata requirements, so supporting several means duplicating work per platform.
Universal Cart
Universal Cart is Google's single virtual cart for adding items from multiple Google surfaces, with checkout handled by each retailer. It addresses the purchase step only, not discovery or experience.
Universal Cart is Google's single virtual cart for adding items from multiple Google surfaces, with checkout handled by each retailer. It addresses the purchase step only, not discovery or experience.
Experience & Purchase
Branded Storefront (on LLMs)
A branded storefront is a brand-controlled shopping experience inside an AI conversation, with the brand's visuals, voice, products, reviews and lifestyle content. It replaces a generic, text-only AI answer with an experience that gives shoppers a reason to choose the brand. Storefronts run as apps within AI platforms and can also run on the brand's own site. DaVinci Commerce's Agentic BrandStore is one example.
A branded storefront is a brand-controlled shopping experience inside an AI conversation, with the brand's visuals, voice, products, reviews and lifestyle content. It replaces a generic, text-only AI answer with an experience that gives shoppers a reason to choose the brand. Storefronts run as apps within AI platforms and can also run on the brand's own site. DaVinci Commerce's Agentic BrandStore is one example.
AI Concierge / Shopping Concierge
An AI concierge is a personalized shopping assistant that helps a shopper choose, answering questions, suggesting looks or pairings and recommending products based on their need. A branded concierge answers from the brand's approved content rather than general web data. This can be hosted on LLMs or on a brand's own website.
An AI concierge is a personalized shopping assistant that helps a shopper choose, answering questions, suggesting looks or pairings and recommending products based on their need. A branded concierge answers from the brand's approved content rather than general web data. This can be hosted on LLMs or on a brand's own website.
Grounded Answers
Grounded answers are AI responses based on verified, approved source content (such as a brand's product data, claims and reviews) rather than the model's general training or unvetted web sources. Grounding keeps product answers accurate and brand-safe.
Grounded answers are AI responses based on verified, approved source content (such as a brand's product data, claims and reviews) rather than the model's general training or unvetted web sources. Grounding keeps product answers accurate and brand-safe.
Path to Purchase / Purchase Hand-off
The path to purchase is the route from an AI conversation to a completed sale. A purchase hand-off sends the shopper to their preferred way to buy, such as the brand's site, a retailer's site or a nearby store, while intent is still high. Agentic checkout is a further path as AI platforms roll it out.
The path to purchase is the route from an AI conversation to a completed sale. A purchase hand-off sends the shopper to their preferred way to buy, such as the brand's site, a retailer's site or a nearby store, while intent is still high. Agentic checkout is a further path as AI platforms roll it out.
Agentic Checkout
Agentic checkout is completing a purchase directly inside an AI platform without leaving the conversation, typically enabled by protocols like ACP and UCP.
Agentic checkout is completing a purchase directly inside an AI platform without leaving the conversation, typically enabled by protocols like ACP and UCP.
Conversational Ads / LLM Ads
Conversational ads are paid placements inside AI conversations that are matched to the context of what a shopper is discussing, rather than to keywords or audience segments. Their effectiveness depends on product data that describes use cases, not just specs.
Conversational ads are paid placements inside AI conversations that are matched to the context of what a shopper is discussing, rather than to keywords or audience segments. Their effectiveness depends on product data that describes use cases, not just specs.
Basket Size
Basket size is the value or number of items in a single purchase. Richer branded experiences inside AI conversations can increase basket size through styling, pairing and cross-sell recommendations.
Basket size is the value or number of items in a single purchase. Richer branded experiences inside AI conversations can increase basket size through styling, pairing and cross-sell recommendations.
Zero-Party Data
Zero-party data is information shoppers share directly and intentionally, such as the needs, preferences and objections they state in a branded AI conversation. Brands can use it across marketing and merchandising.
Zero-party data is information shoppers share directly and intentionally, such as the needs, preferences and objections they state in a branded AI conversation. Brands can use it across marketing and merchandising.
DaVinci Commerce Platform Terms
Agentic Commerce Experience Platform (ACEP)
The Agentic Commerce Experience Platform is DaVinci Commerce's no-code, always-on platform for getting products discovered, chosen and bought by AI agents and shoppers. It runs one continuous loop (measurement, enrichment, machine-readable discoverability, experience and purchase) through the Agentic Discoverability Engine, with Brand Agent delivering to AI agents, and the Agentic BrandStore.
The Agentic Commerce Experience Platform is DaVinci Commerce's no-code, always-on platform for getting products discovered, chosen and bought by AI agents and shoppers. It runs one continuous loop (measurement, enrichment, machine-readable discoverability, experience and purchase) through the Agentic Discoverability Engine, with Brand Agent delivering to AI agents, and the Agentic BrandStore.
The ACEP Loop
The ACEP loop is DaVinci Commerce's five-stage closed loop: Measurement → Enrichment → Discoverability → Experience → Purchase. The Agentic Discoverability Engine handles measurement and enrichment, with Brand Agent driving machine-readable discoverability. The Agentic BrandStore handles experience and purchase.
The ACEP loop is DaVinci Commerce's five-stage closed loop: Measurement → Enrichment → Discoverability → Experience → Purchase. The Agentic Discoverability Engine handles measurement and enrichment, with Brand Agent driving machine-readable discoverability. The Agentic BrandStore handles experience and purchase.
Agentic Discoverability Engine
The Agentic Discoverability Engine is DaVinci Commerce's always-on engine for getting products recommended by AI agents. It measures attributed discoverability for every SKU, enriches each product with shopper context stored in Product Context Memory and, through Brand Agent, delivers the enriched catalog in machine-readable formats. Then it re-measures the lift, continuously.
The Agentic Discoverability Engine is DaVinci Commerce's always-on engine for getting products recommended by AI agents. It measures attributed discoverability for every SKU, enriches each product with shopper context stored in Product Context Memory and, through Brand Agent, delivers the enriched catalog in machine-readable formats. Then it re-measures the lift, continuously.
Brand Agent
Brand Agent is the part of DaVinci Commerce's Agentic Discoverability Engine that formats a brand's enriched product catalog and delivers it to AI shopping agents such as ChatGPT, Gemini, Claude and Meta Muse via ACP, UCP, JSON-LD and MCP. It is agent-to-agent: the shopping agent asks, the Brand Agent answers. Agentic BrandStore is the agent-to-shopper counterpart.
Brand Agent is the part of DaVinci Commerce's Agentic Discoverability Engine that formats a brand's enriched product catalog and delivers it to AI shopping agents such as ChatGPT, Gemini, Claude and Meta Muse via ACP, UCP, JSON-LD and MCP. It is agent-to-agent: the shopping agent asks, the Brand Agent answers. Agentic BrandStore is the agent-to-shopper counterpart.
Agentic BrandStore
The Agentic BrandStore is DaVinci Commerce's branded shopping experience inside AI platforms and on brand sites. It talks to shoppers directly, answering from the brand's product catalog, approved content and reviews in a visual, brand-controlled storefront with a personalized concierge, then hands off to the brand's site, a retailer's site or a nearby store.
The Agentic BrandStore is DaVinci Commerce's branded shopping experience inside AI platforms and on brand sites. It talks to shoppers directly, answering from the brand's product catalog, approved content and reviews in a visual, brand-controlled storefront with a personalized concierge, then hands off to the brand's site, a retailer's site or a nearby store.
Product Context Memory
Product Context Memory is DaVinci Commerce's patent-pending, dynamic product knowledge graph. It continuously draws on reviews, social content, influencer content and the open web to map each SKU to the pain points, life moments and use cases shoppers actually describe, so a single product can be matched to thousands of prompts. It powers both the Agentic Discoverability Engine and the Agentic BrandStore.
Product Context Memory is DaVinci Commerce's patent-pending, dynamic product knowledge graph. It continuously draws on reviews, social content, influencer content and the open web to map each SKU to the pain points, life moments and use cases shoppers actually describe, so a single product can be matched to thousands of prompts. It powers both the Agentic Discoverability Engine and the Agentic BrandStore.
Product Discovery Insights
Product Discovery Insights is DaVinci Commerce's SKU-level attributed discoverability measurement tool. It shows where each product's direct link to a sale is winning or losing, across every major LLM, for key prompts and against competitors, then re-measures after enrichment so the lift is proven.
Product Discovery Insights is DaVinci Commerce's SKU-level attributed discoverability measurement tool. It shows where each product's direct link to a sale is winning or losing, across every major LLM, for key prompts and against competitors, then re-measures after enrichment so the lift is proven.
SKU-Level Attributed Discovery Measurement
SKU-level attributed discovery measurement tracks whether each individual product is recommended with a purchase link for specific prompts. It attributes discoverability gaps, and the lift from enrichment, to that product, rather than reporting one brand-wide score.
SKU-level attributed discovery measurement tracks whether each individual product is recommended with a purchase link for specific prompts. It attributes discoverability gaps, and the lift from enrichment, to that product, rather than reporting one brand-wide score.
Enrichment Lift Testing
Enrichment lift testing A/B tests a SKU's raw and enriched product data to measure the effect on its discoverability across LLMs before rolling the enrichment out. It shows which product context actually gets each product discovered.
Enrichment lift testing A/B tests a SKU's raw and enriched product data to measure the effect on its discoverability across LLMs before rolling the enrichment out. It shows which product context actually gets each product discovered.
