ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping
The wave of AI-native applications is moving shopping beyond page- and feed-based browsing toward intent-driven experiences orchestrated by LLM agents. A common design wraps an LLM around existing search and recommendation pipelines, forcing complex intents through low-bandwidth retrieval or ranking interfaces and leaving a gap between language understanding and item-space fulfillment. Generative recommendation gives LLMs a direct item-space interface through semantic IDs (SIDs), but existing models mainly generate candidates for retrieval rather than translate flexible intents into item-space
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- LinkedLinked via unknownRepositioning retail for the AI era →
- LinkedLinked via unknownagent-tools →
- LinkedLinked via unknownAgentic Resource Discovery: Let agents search →
- PossiblePossibly related (embedding) · 28%neuml/txtai →
“Possibly related via embedding similarity 0.55 (not asserted). Timestamp check: artifact after paper (+13d).”
- PossiblePossibly related (embedding) · 54%RimantasZ/contextspy →
- PossiblePossibly related (embedding) · 51%I built a Claude agent that runs Instagram DM ordering for a 7-location sushi chain →
- PossiblePossibly related (embedding) · 58%How DoorDash Built an AI Shopping Assistant That Doesn’t Rely on the LLM Alone - infoq.com →
- FuzzySimilar title/name (fuzzy) · 59%Fosowl/agenticSeek →
“Fuzzy title match (0.73): “ShopX: A Foundation Model for Intent-to-Item Fulfillment in ” ≈ “Fosowl/agenticSeek””