Exploring AI Virtual Assistants for E-commerce

“Which foundation shade am I?” That one question dominated a recent brand conversation. It points to a bigger shift. The industry used to talk about AI in CX in defensive terms: deflect tickets, cut handle time, lower costs. Now customers are showing us how to use AI on offense: the moment they ask for help choosing what to buy. Shade match is just one of what I see an emerging pattern everywhere: 👗 AI Stylist: “Build me a look for a summer wedding.” 📏 AI Fit Finder: “What size will actually fit me in this brand?” 🧴 AI Skin Analyzer: “Here’s a quick selfie. What routine should I follow?” 🎁 AI Gift Finder: “I’ve got $100. What would she love?” An ecommerce leader said to me last week, “AI that helps customers choose is easier to fund than AI that just deflects.” When an assistant guides the decision and suggests companion products, brands see higher intent captured, better bundles, and stronger AOV. Even after hours 🌙 It’s time to move your AI strategy from defense to offense. 🛡️ Defense = save money: ticket deflection, cost reduction, 24/7 availability ⚔️ Offense = make money: personalized recommendations, instant consultations, higher AOV Old metric: ticket resolution time ⏱️ New metric: AI assisted revenue 💰📈 While others focus on answering questions, you can use AI to drive sales. The question leaders should be asking is not, “How can AI lower our support costs?” It is, “How can we deploy an AI sales agent to be our top performer?” #ecommerce

The last few weeks have been intense. I’ve been deep-diving into how AI and LLMs can transform the way we interact with Shopify data—not just for automation, but for smarter decision-making. So I built something small MVP. A chatbot that pulls real-time product, customer, and order data from Shopify, pushes it to vector DBs like Chroma, Pinecone, Milvus, and makes it searchable with OpenAI embeddings. You ask: “Where is my order?” → It checks login and gives you a contextual reply. You say: “Show me a red t-shirt under $30” → It fetches product data semantically. It’s not just for customer support—imagine CXOs chatting with their business data to get instant answers like: “What’s the best-selling product in California last month?” I wrote a deep-dive blog on how I built it, with all the tech breakdowns: - Shopify API - OpenAI embeddings - Vector DB - LLM orchestration Would love to hear your thoughts on similar use cases or how you’re approaching AI in eCommerce. #Shopify #AI #LLM #OpenAI #eCommerce #CustomerSupport #TechForBusiness #GenerativeAI #CRO #CXO

I built my first Agentic Commerce startup before LLMs existed at scale.  I’ve been tracking the agentic commerce & payments space closely now and there's some interesting stuff happening right now. The big players are going in different directions. Shopify is doubling down on their Sidekick AI for merchant automation. Amazon is quietly building out their fulfillment agents behind the scenes. And then you have newer companies focusing specifically on customer service automation for e-commerce. But here's what I'm seeing as the real trend: it's not just about chatbots anymore. The companies that are winning are building agents that can actually take actions - process returns, update inventory, coordinate with suppliers, even make purchasing decisions within set parameters. The most interesting work is happening in the mid-market space. Small businesses can't afford custom solutions, and enterprise has their own teams. But mid-market retailers are perfect for these agentic commerce tools. I'm seeing three main categories emerging: 1) Customer service agents that can actually resolve issues (not just answer questions) 2) Inventory management agents that predict and auto-reorder stock 3) Marketing agents that can adjust campaigns based on real-time performance data Here are some other patterns I’m seeing that are using AI across entire customer journey: Product Discovery: AI agents are now scanning millions of product reviews, social mentions, and search trends to predict what customers want before they know it themselves. Dynamic Pricing: Gone are the days of static price tags. AI is analyzing competitor pricing, inventory levels, demand patterns, and customer behavior in real-time. e-commerce sites are updating prices thousands of times per day. Fraud Detection: Traditional rule-based systems caught maybe 50-60% of fraudulent transactions. Modern AI systems are hitting 85%+ accuracy while reducing false positives that frustrate legitimate customers. Payment Optimization: AI is figuring out which payment method to suggest to each customer, when to retry failed payments, and how to route transactions for the lowest fees and highest success rates. Customer Support: Payment issues used to require human agents. Now AI can resolve 80% of payment disputes, refund requests, and billing questions without human intervention. The companies moving fast on this are seeing dramatic improvements in conversion rates, customer satisfaction, and operational efficiency. The ones waiting will be falling behind quickly.

McKinsey & Company: "𝗧𝗵𝗮𝘁'𝘀 𝗛𝗼𝘄 𝗖𝗜𝗢𝘀 𝗮𝗻𝗱 𝗖𝗧𝗢𝘀 𝗖𝗮𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗠𝗮𝘅𝗶𝗺𝘂𝗺 𝗜𝗺𝗽𝗮𝗰𝘁" This McKinsey & Co report highlights how #GenAI, when deeply integrated, can revolutionize business operations. I took a stab at CPG eCommerce use case below, and thriving with generative #AI isn’t about just deploying a model; it demands a deep integration into your enterprise stack. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 𝗠𝘂𝗹𝘁𝗶-𝗹𝗮𝘆𝗲𝗿𝗲𝗱 𝗚𝗲𝗻𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗖𝗣𝗚⬇️ 𝟭. 𝗖𝘂𝘁𝗼𝗺𝗲𝗿 𝗟𝗮𝘆𝗲𝗿: → The user logs in, browses personalized product recommendations, and either finalizes a purchase or escalates to a support agent—all seamlessly without grasping the backend processes. This layer prioritizes trust, rapid responses, and tailored suggestions like skincare routines based on user preferences. 📍Business Impact: Boosts customer satisfaction and loyalty, increasing conversion rates by up to 40% through hyper-personalized interactions that drive repeat purchases. 𝟮. 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 → Oversees user engagement: - Chatbot launches and steers the dialogue, suggesting complementary products - Escalation to a human agent activates if AI can't fully address complex queries, like ingredient allergies 📍Business Impact: Enhances efficiency in consumer support, reducing resolution times and operational costs while minimizing cart abandonment in #eCommerce flows. 𝟯. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗟𝗮𝘆𝗲𝗿: → Performs smart actions using context: - Retrieves user profile data - Validates promotions and inventory - Creates customized options, such as virtual try-ons - Advances the process, like adding to the cart 📍Business Impact: Accelerates innovation in product discovery, lifting marketing productivity by 10-40% and enabling dynamic pricing that optimizes revenue in competitive #FMCG markets. 𝟰. 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗔𝗽𝗽 𝗟𝗮𝘆𝗲𝗿 → Links AI to essential enterprise platforms: - User verification and access management - Promotion rules and order processing - Support agent routing algorithms 📍Business Impact: Streamlines supply chain and sales workflows, cutting technical debt by 20-40% and improving inventory accuracy to reduce stockouts and overstock costs. 𝟱. 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿 → Delivers instant contextual details: - Consumer profiles - Purchase records - Promotion guidelines - Support team directories 📍Business Impact: Powers precise AI insights, enhancing demand forecasting and personalization to minimize waste in perishable goods while boosting overall data-driven decision-making. 𝟲. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗟𝗮𝘆𝗲𝗿 → Supports scalability, efficiency, and oversight: - Cloud or hybrid setups - AI model coordination - High-speed response handling - Privacy and compliance controls 📍Business Impact: Ensures robust, secure operations at scale, unlocking value by optimizing resource use, slashing IT ops costs.

𝗧𝗟;𝗗𝗥: Amazon's multi agent design in 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗔𝗴𝗲𝗻𝘁𝘀 orchestrates specialized AI workers that transform how 1M+ sellers run their businesses leading to outsize outcomes. 𝗙𝗿𝗼𝗺 𝗗𝗮𝘁𝗮 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝘁𝗼 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 E-commerce sellers face a paradox: rich tools everywhere, insights nowhere. Amazon's response? 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗔𝗴𝗲𝗻𝘁𝘀 (IA)—an LLM-based multi-agent system that lets sellers simply ask: "𝘞𝘩𝘢𝘵 𝘸𝘦𝘳𝘦 𝘮𝘺 𝘵𝘰𝘱 10 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘴 𝘭𝘢𝘴𝘵 𝘮𝘰𝘯𝘵𝘩?" or "𝘏𝘰𝘸 𝘥𝘰𝘦𝘴 𝘮𝘺 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘤𝘰𝘮𝘱𝘢𝘳𝘦 𝘵𝘰 𝘣𝘦𝘯𝘤𝘩𝘮𝘢𝘳𝘬𝘴?" (Read more here: https://bit.ly/41cbt4R) No more hunting through dashboards. Just natural conversation yielding precise data insights. 𝗧𝗵𝗲 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 IA's hierarchical manager-worker structure optimizes for coverage, accuracy, and latency: 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗔𝗴𝗲𝗻𝘁:  • Lightweight encoder-decoder for Out-of-Domain detection (96.9% precision)  • BERT-based classifier for agent routing (83% accuracy, 0.31s latency)  • Query augmentation for temporal disambiguation  • Parallel processing to minimize latency 𝗪𝗼𝗿𝗸𝗲𝗿 𝗔𝗴𝗲𝗻𝘁𝘀:  • Data Presenter: Handles descriptive analytics ("Show me sales trends")  • Insight Generator: Provides diagnostic analysis ("How is my business performing?") 𝗧𝗵𝗲 𝗦𝗲𝗰𝗿𝗲𝘁 𝗦𝗮𝘂𝗰𝗲: 𝗥𝗼𝗯𝘂𝘀𝘁 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹 Unlike fragile text-to-SQL approaches, IA leverages:  • API-based data retrieval with built-in constraints  • Divide-and-conquer query decomposition  • Dynamic domain knowledge injection  • Strategic planning for granular data aggregation 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲  • 89.5% question-level accuracy  • <15s P90 latency  • 97.7% relevancy score  • 95.8% correctness score All of this is powered by of course Amazon Web Services (AWS) Bedrock and SageMaker. Currently live for Amazon US sellers, transforming how businesses interact with their data. Great work by Jincheng Bai and team! 𝗧𝗵𝗲 𝗔𝗺𝗮𝘇𝗼𝗻 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 Insight Agents isn't just another chatbot—it's a force multiplier for sellers. By combining lightweight specialized models with strategic LLM deployment, Amazon delivers enterprise-grade insights at conversational speed. The future of business intelligence isn't more dashboards. It's intelligent agents that understand your questions and deliver precise, actionable insights.