[{"data":1,"prerenderedAt":92},["ShallowReactive",2],{"ind-detail-retail-ecommerce":3,"ind-work-retail-ecommerce":50,"ill-industries\u002Fretail-ecommerce":91},{"slug":4,"title":5,"icon":6,"summary":7,"challenges":8,"useCases":13,"relatedServices":32,"faq":37,"seo":47},"retail-ecommerce","Retail & E-commerce","bag","AIYatch helps retailers and e-commerce brands raise conversion and cut cost-to-serve with AI: support and shopping assistants across web, WhatsApp and voice, personalised recommendations and search, demand and stock forecasting, and review mining. We integrate directly with Shopify, WooCommerce, ERPs and helpdesks so results show up in the tools your team already uses.",[9,10,11,12],"Support tickets about orders, returns and delivery repeat constantly and peak in seasonal spikes.","Shoppers abandon when search and recommendations do not understand what they mean.","Over- and under-stocking erode margin because forecasts are built in spreadsheets.","Reviews, chats and social feedback contain insight nobody has time to read.",[14,17,20,23,26,29],{"title":15,"text":16},"Order-status and returns assistants","Chatbots and voice agents that check order status, start returns and answer policy questions by talking to your store and logistics APIs, escalating edge cases to humans.",{"title":18,"text":19},"Conversational shopping and product advice","LLM assistants that recommend products from your catalogue, compare options and answer sizing or compatibility questions with grounded, up-to-date data.",{"title":21,"text":22},"Semantic search and recommendations","Vector search and recommendation engines that understand intent, handle typos and long queries, and personalise results by behaviour.",{"title":24,"text":25},"Demand and stock forecasting","Time-series models by SKU, store and channel that inform purchasing, replenishment and promotion planning.",{"title":27,"text":28},"Review and feedback mining","NLP pipelines that classify sentiment, extract product issues and surface trends from reviews, chats and surveys into dashboards.",{"title":30,"text":31},"Marketing content generation","Generative AI that drafts product descriptions, campaign copy and localised variants inside brand guidelines with human review.",[33,34,35,36],"ai-chatbots","data-analytics-predictive-ai","generative-ai-llm","ai-integration",[38,41,44],{"q":39,"a":40},"Can the assistant work on WhatsApp and our website at the same time?","Yes. We build one assistant with a shared knowledge base and connect it to multiple channels (website widget, WhatsApp, Instagram, Messenger, email and voice) with channel-specific formatting. Conversation history and hand-off to human agents are consistent across all of them.",{"q":42,"a":43},"How does AI improve e-commerce search?","Semantic search maps queries and products into the same vector space, so “warm jacket for hiking in rain” finds relevant items even without matching keywords. Combined with keyword search, filters and behavioural signals, it improves relevance, reduces zero-result searches and lifts conversion.",{"q":45,"a":46},"How accurate is AI demand forecasting?","It depends on data history, seasonality and how many SKUs have sparse sales. We benchmark against your current method during discovery and report error metrics honestly; the typical goal is a measurable reduction in forecast error and stock-outs, not perfection.",{"title":48,"description":49},"AI for Retail & E-commerce: Assistants & Search | AIYatch","AI for retailers and e-commerce brands: support and shopping assistants, semantic search, recommendations, demand forecasting and review mining integrated with Shopify and your stack.",[51,68,80],{"path":52,"title":53,"description":54,"industry":5,"services":55,"results":57,"cover":67,"coverAlt":67},"\u002Fwork\u002Fecommerce-support-assistant","ChatGPT-powered support assistant for an online retailer","A GPT-based assistant grounded in order data and help-centre content that now handles the majority of first-line customer inquiries.",[33,56,35],"rag-systems",[58,61,64],{"value":59,"label":60},"80%","of inquiries resolved without an agent",{"value":62,"label":63},"60%","faster first response",{"value":65,"label":66},"+40%","customer satisfaction (CSAT)",null,{"path":69,"title":70,"description":71,"industry":5,"services":72,"results":73,"cover":67,"coverAlt":67},"\u002Fwork\u002Fmarketing-eda","Exploratory data analysis for a marketing team","A rigorous analysis of campaign, CRM and web data that reallocated budget toward the channels that actually converted.",[34],[74,77],{"value":75,"label":76},"+18%","conversion after budget reallocation",{"value":78,"label":79},"1","shared source of truth for marketing KPIs",{"path":81,"title":82,"description":83,"industry":5,"services":84,"results":85,"cover":67,"coverAlt":67},"\u002Fwork\u002Fecommerce-personalisation-plugin","Personalisation plugin for an e-commerce platform","Recommendation and search-ranking models packaged as a plugin for the client's storefront.",[34,36],[86,88],{"value":75,"label":87},"sales from recommended products",{"value":89,"label":90},"\u003C80 ms","recommendation latency","\u003Csvg xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"0 0 640 480\" role=\"img\" aria-hidden=\"true\">\u003Cg fill=\"none\" stroke=\"currentColor\" stroke-width=\"5\" stroke-linecap=\"round\" stroke-linejoin=\"round\">\u003Cpath d=\"M130 150H180L220 340H490L530 210H195\"\u002F>\u003Cline x1=\"208\" y1=\"275.4\" x2=\"505\" y2=\"275.4\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Ccircle cx=\"250\" cy=\"380\" r=\"16\"\u002F>\u003Ccircle cx=\"460\" cy=\"380\" r=\"16\"\u002F>\u003Cg stroke-width=\"3.5\">\u003Crect x=\"270\" y=\"220\" width=\"60\" height=\"60\" rx=\"8\"\u002F>\u003Crect x=\"350\" y=\"232\" width=\"52\" height=\"48\" rx=\"8\"\u002F>\u003Cline x1=\"300\" y1=\"220\" x2=\"300\" y2=\"250\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"376\" y1=\"232\" x2=\"376\" y2=\"256\" stroke-opacity=\"0.45\"\u002F>\u003C\u002Fg>\u003Cg stroke-width=\"3.5\">\u003Cpath d=\"M442 60H558A12 12 0 0 1 570 72V114A12 12 0 0 1 558 126H551L554 142L536 126H442A12 12 0 0 1 430 114V72A12 12 0 0 1 442 60Z\"\u002F>\u003C\u002Fg>\u003Ccircle cx=\"475\" cy=\"93\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Ccircle cx=\"500\" cy=\"93\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Ccircle cx=\"525\" cy=\"93\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003C\u002Fg>\u003C\u002Fsvg>",1792973679705]