{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-claros-ai-shopper","slug":"claros-ai-shopper","name":"Claros AI Shopper","type":"product","url":"https://www.claros.so/","page_url":"https://unfragile.ai/claros-ai-shopper","categories":["app-builders"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-claros-ai-shopper__cap_0","uri":"capability://text.generation.language.natural.language.product.preference.learning","name":"natural language product preference learning","description":"Learns user taste preferences through conversational natural language input, building an implicit preference model that captures style, budget, category interests, and aesthetic preferences without requiring explicit structured forms. Uses dialogue-based preference extraction to iteratively refine understanding of what products match user intent through multi-turn conversation.","intents":["I want to tell an AI what kind of products I like without filling out a survey","I need the system to understand my style preferences from casual conversation","I want to refine my product search by describing what I'm looking for in natural language"],"best_for":["E-commerce platforms wanting conversational product discovery","Shoppers who prefer dialogue over form-based filtering","Retailers building personalized shopping experiences"],"limitations":["Preference model accuracy depends on conversation depth — shallow interactions may produce generic recommendations","No explicit preference export or portability — preferences are session-bound unless persisted to user profile","Cannot disambiguate between stated preferences and actual purchase behavior without transaction data integration"],"requires":["User account or session to maintain conversation context","Product catalog with metadata (category, price, description, attributes)","LLM capable of multi-turn dialogue (GPT-4 level or equivalent)"],"input_types":["natural language text","conversational utterances"],"output_types":["preference embeddings or vectors","structured preference tags","ranked product recommendations"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-claros-ai-shopper__cap_1","uri":"capability://search.retrieval.cross.catalog.product.search.and.matching","name":"cross-catalog product search and matching","description":"Searches across multiple product catalogs (retailers, marketplaces, brands) to find items matching learned user preferences, using semantic matching to align user intent with product metadata and descriptions. Likely implements vector-based similarity search or embedding-based retrieval to match preference profiles against product embeddings indexed from multiple sources.","intents":["I want to find products that match my taste across multiple stores, not just one retailer","I need the AI to search beyond a single marketplace to find the best match for my style","I want recommendations from different brands and retailers in one place"],"best_for":["Shoppers wanting unified product discovery across multiple retailers","Affiliate or comparison shopping platforms","Retailers integrating third-party catalog data for expanded selection"],"limitations":["Search quality depends on catalog metadata richness — sparse or inconsistent product descriptions reduce matching accuracy","Real-time pricing and availability data may lag if catalogs are not continuously synced","Cross-catalog deduplication is non-trivial — similar products from different retailers may be ranked separately","API rate limits from partner catalogs may throttle search throughput"],"requires":["Integrations with product data APIs or feeds from multiple retailers","Product embedding model trained on cross-catalog product descriptions","Vector database or semantic search index (e.g., Pinecone, Weaviate, Milvus)","Real-time or near-real-time catalog sync mechanism"],"input_types":["user preference profile","product search queries","natural language descriptions"],"output_types":["ranked list of products with source/retailer","product metadata (price, availability, URL)","relevance scores"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-claros-ai-shopper__cap_2","uri":"capability://planning.reasoning.taste.based.product.ranking.and.personalization","name":"taste-based product ranking and personalization","description":"Ranks search results and recommendations based on learned user taste preferences, using a personalization model that weights product attributes (style, price range, brand, category) against user preference vectors. Likely implements a learning-to-rank approach or collaborative filtering variant that reorders canonical product lists based on individual preference profiles.","intents":["I want search results ordered by how well they match my personal style, not just popularity or price","I need the system to prioritize products that fit my taste over generic bestsellers","I want personalized ranking that improves as the system learns my preferences"],"best_for":["E-commerce platforms with diverse user bases and varied taste profiles","Fashion and lifestyle retailers where subjective preference matters more than objective specs","Personalization engines that need to move beyond collaborative filtering"],"limitations":["Cold-start problem for new users — ranking quality is poor until sufficient preference data is collected","Preference drift over time is not explicitly modeled — system may not adapt if user taste changes seasonally or long-term","Ranking model bias toward products with rich metadata — sparse product descriptions may be systematically downranked","No explicit diversity control — personalized ranking may create filter bubbles by over-emphasizing similar products"],"requires":["User preference profile or embedding vector","Product attribute vectors or embeddings","Ranking model (learning-to-rank, neural network, or heuristic weighting)","Training data from user interactions (clicks, purchases, ratings) to tune ranking weights"],"input_types":["user preference vector","product candidate list","product metadata and embeddings"],"output_types":["ranked product list","relevance scores per product","personalization explanations (optional)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-claros-ai-shopper__cap_3","uri":"capability://planning.reasoning.interactive.preference.refinement.through.feedback","name":"interactive preference refinement through feedback","description":"Allows users to provide feedback on recommendations (thumbs up/down, 'show me more like this', 'not my style') which are fed back into the preference model to iteratively refine taste understanding. Implements a feedback loop that updates the user preference vector or re-weights preference attributes based on explicit signals, improving subsequent recommendations without requiring users to restart the conversation.","intents":["I want to tell the AI 'that's not quite right' and have it adjust its recommendations","I need to refine my preferences by reacting to specific product suggestions","I want the system to learn from my feedback and improve over time"],"best_for":["Interactive recommendation systems where user feedback is abundant","Conversational shopping assistants that need real-time preference adaptation","Platforms with high user engagement where feedback collection is feasible"],"limitations":["Feedback signal quality varies — thumbs-down may indicate poor matching or poor product quality, not preference mismatch","Feedback can be contradictory or noisy, requiring signal filtering or confidence weighting","Preference model updates must be fast enough for interactive response times (<500ms) or feedback loop feels broken","No mechanism to distinguish between 'I don't like this product' and 'I don't like this category' without explicit clarification"],"requires":["Feedback collection UI (thumbs up/down, skip, save, etc.)","Preference model that supports incremental updates","Fast inference path for re-ranking after feedback (sub-second latency)","Feedback storage and audit trail for debugging and analysis"],"input_types":["explicit feedback signals (binary, categorical, or scalar)","implicit signals (dwell time, clicks, saves)","natural language feedback ('too expensive', 'wrong color')"],"output_types":["updated preference vector","re-ranked product recommendations","feedback acknowledgment and explanation"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-claros-ai-shopper__cap_4","uri":"capability://automation.workflow.product.discovery.automation.and.shopping.workflow","name":"product discovery automation and shopping workflow","description":"Automates the end-to-end shopping discovery workflow by orchestrating conversation, search, ranking, and transaction steps into a cohesive agent that can autonomously find and surface products matching user intent. Implements a multi-step workflow where the AI maintains conversation state, executes searches, filters results, and presents curated selections without requiring users to manually navigate multiple steps.","intents":["I want an AI to handle the entire shopping discovery process from preference elicitation to final recommendations","I need the system to proactively suggest products without me having to search manually","I want a shopping assistant that understands context and can make intelligent suggestions"],"best_for":["E-commerce platforms automating customer discovery workflows","Mobile shopping apps where conversational UX is preferred over traditional browsing","Retailers wanting to reduce friction in the product-finding process"],"limitations":["Workflow automation is opaque to users — if the system makes a wrong decision (e.g., wrong product category), users may not understand why","Multi-step orchestration introduces latency — each step (conversation, search, ranking) adds ~100-500ms, compounding to 1-2s total response time","Workflow state management is complex — conversation context, search filters, and ranking preferences must be synchronized across steps","No built-in transaction handling — checkout and payment integration is out of scope"],"requires":["Conversation management system (state machine or prompt-based orchestration)","Search and ranking pipeline (from previous capabilities)","Workflow orchestration framework (e.g., LangChain, LlamaIndex, or custom agent loop)","Product catalog and inventory system","User session management"],"input_types":["natural language user input","conversation history","user preferences and profile"],"output_types":["curated product recommendations","product details and links","conversation responses","transaction-ready product selections"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-claros-ai-shopper__cap_5","uri":"capability://memory.knowledge.multi.turn.conversational.context.management","name":"multi-turn conversational context management","description":"Maintains conversation state across multiple turns, tracking user intent, preferences mentioned in earlier messages, and conversation history to enable coherent multi-turn dialogue. Implements context windowing and summarization to keep relevant conversation history within LLM context limits while discarding irrelevant details, allowing users to reference earlier preferences without re-stating them.","intents":["I want to mention my style once and have the AI remember it throughout the conversation","I need the system to understand references to earlier preferences ('like the blue one you showed me')","I want to have a natural back-and-forth conversation without repeating myself"],"best_for":["Conversational AI systems requiring multi-turn coherence","Shopping assistants where users expect stateful dialogue","Any dialogue system where context accumulation improves quality"],"limitations":["Context window limits force trade-offs between conversation length and detail — long conversations may lose early context","Context summarization is lossy — important nuances may be lost when compressing conversation history","No explicit memory of past sessions — context resets between conversations unless explicitly persisted","Ambiguous references ('that one', 'the other style') require disambiguation logic that may fail in complex conversations"],"requires":["Conversation history storage (in-memory or database)","Context windowing strategy (e.g., sliding window, summarization, or hierarchical compression)","LLM with sufficient context window (4K+ tokens recommended)","Prompt engineering to maintain context awareness across turns"],"input_types":["user utterance","conversation history","extracted preferences and entities"],"output_types":["contextually-aware response","updated conversation state","extracted or updated preferences"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"high","permissions":["User account or session to maintain conversation context","Product catalog with metadata (category, price, description, attributes)","LLM capable of multi-turn dialogue (GPT-4 level or equivalent)","Integrations with product data APIs or feeds from multiple retailers","Product embedding model trained on cross-catalog product descriptions","Vector database or semantic search index (e.g., Pinecone, Weaviate, Milvus)","Real-time or near-real-time catalog sync mechanism","User preference profile or embedding vector","Product attribute vectors or embeddings","Ranking model (learning-to-rank, neural network, or heuristic weighting)"],"failure_modes":["Preference model accuracy depends on conversation depth — shallow interactions may produce generic recommendations","No explicit preference export or portability — preferences are session-bound unless persisted to user profile","Cannot disambiguate between stated preferences and actual purchase behavior without transaction data integration","Search quality depends on catalog metadata richness — sparse or inconsistent product descriptions reduce matching accuracy","Real-time pricing and availability data may lag if catalogs are not continuously synced","Cross-catalog deduplication is non-trivial — similar products from different retailers may be ranked separately","API rate limits from partner catalogs may throttle search throughput","Cold-start problem for new users — ranking quality is poor until sufficient preference data is collected","Preference drift over time is not explicitly modeled — system may not adapt if user taste changes seasonally or long-term","Ranking model bias toward products with rich metadata — sparse product descriptions may be systematically downranked","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=claros-ai-shopper","compare_url":"https://unfragile.ai/compare?artifact=claros-ai-shopper"}},"signature":"gvLcFkcPNgi6c3F+Gt6RwMtKSJwj3sAZrCnnWJ3VhwrK2uuRmaW+iknqFXfb90DbHI9Itksu4I/HiLaZr0TIDw==","signedAt":"2026-06-20T08:26:32.961Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/claros-ai-shopper","artifact":"https://unfragile.ai/claros-ai-shopper","verify":"https://unfragile.ai/api/v1/verify?slug=claros-ai-shopper","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}