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AGENTICSHOPPING.XYZManage supplier listing
Agentic Shopping — Two linked layers. The measurement layer records when large language models surface a brand in response to buying-intent prompts across ChatGPT, Perplexity and Google AI and Gemini, then attributes those conversations through to actual revenue — producing prompt-to-purchase pairs, which competitor wins a given prompt, and a share-of-revenue-influenced-by-AI measure broken down by channel, model and product. The second layer is the byproduct of a real deployed shopping agent: Amy operates as an active buyer who researches options, compares prices, applies discounts and compl…
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Two linked layers. The measurement layer records when large language models surface a brand in response to buying-intent prompts across ChatGPT, Perplexity and Google AI and Gemini, then attributes those conversations through to actual revenue — producing prompt-to-purchase pairs, which competitor wins a given prompt, and a share-of-revenue-influenced-by-AI measure broken down by channel, model and product. The second layer is the byproduct of a real deployed shopping agent: Amy operates as an active buyer who researches options, compares prices, applies discounts and completes purchases, while simultaneously auditing the brands she shops from, identifying discovery, parsing and checkout blockers and issuing prioritised fixes. Delivery of the services layer also implies operational artefacts — machine-parseable structured product feeds with full variant data syndicated to agents, ACP endpoints issuing delegated vault tokens under explicit allowance constraints, Shared Payment Token integration with tokens described as scoped, bounded and observable, and agent-aware fraud-stack retuning data separating legitimate agent traffic from agent-specific attack patterns
Coverage Consumer Discretionary · Information TechnologyAsset class EquitiesTickers SHOP · AMZN · WMT
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Agentic Shopping — Two linked layers. The measurement layer records when large language models surface a brand in response to buying-intent prompts across ChatGPT, Perplexity and Google AI and Gemini, then attributes those conversations through to actual revenue — producing prompt-to-purchase pairs, which competitor wins a given prompt, and a share-of-revenue-influenced-by-AI measure broken down by channel, model and product. The second layer is the byproduct of a real deployed shopping agent: Amy operates as an active buyer who researches options, compares prices, applies discounts and compl…
Agentic Shopping offers (Alternative, Sentiment, Reference) — Unquantified but potentially the densest per-row asset in this group if Amy is genuinely running shopping sessions: every audit is a live agent task against a real merchant, generating prompt, retrieval, parse, comparison, discount and checkout events with a pass-or-fail outcome. The prompt-to-purchase attribution corpus is also compounding and non-reconstructable, since which model surfaced which brand for which prompt on a given day cannot be recovered later. No counts are published, so treat all volume as unverified pending a sample.
Training and evaluating shopping agents on real failure points — where agents abandon checkout, fail to parse a catalogue, or lose a comparison is directly usable as supervision for agent capability models; building AI-recommendation win-loss models predicting which brand an LLM will surface for a buying-intent prompt; cross-model preference comparison measuring how GPT, Gemini and Perplexity disagree on the same commercial query, which is a benchmark no single-model vendor produces; retail-media and advertising attribution where the audience is an agent rather than a human; agent-aware fraud and bot-classification training with legitimate-agent labels; and catalogue-structure research on how product feeds must be shaped to be machine-parseable
Coverage spans US; Internet & Direct Marketing Retail, Application Software; alternative, sentiment, reference; equities.
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