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47 Listings in E-commerce AI Available
What is Lily AI? Lily AI, through its Lily Max platform, is an agentic product intelligence system for retailers. It enriches product catalog data and measures the performance lift across paid, organic and agentic surfaces. Key capabilities of Lily AI Product data enrichment: Automatic attributes to make catalogs AI-ready. Controlled A/B testing: Measures lift with confidence intervals. Google Shopping optimization: Enriched feed data for ads and listings. Meta Advantage+ optimization: Improves catalog data used in Meta campaigns. AI discovery: Targets recommendations in ChatGPT and Gemini. Onsite search: Improves relevance of retailer site search. How Lily AI works Lily connects through a Google Merchant Center feed, enriches product attributes and then runs controlled tests to measure lift. It works within existing commerce stacks without replatforming. Results are measured across Google Ads, Meta Ads, AI discovery and onsite search. Who uses Lily AI? Lily AI is used by fashion, footwear, beauty and general retailers. The vendor lists customers including Coach, M&S, NARS, Shiseido, Vuori, Fabletics, J.Crew, HOKA and UGG. Lily AI pricing Lily AI does not publish prices. It offers a free 30-day trial on 500 catalog products, after which buyers book a demo to expand. Lily AI alternatives Alternatives include Syte, which focuses on visual search and AI tagging, Constructor, which provides ecommerce search and recommendations, and Algolia, which offers search and discovery APIs.
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What is Klevu? Klevu is an AI search and product discovery product for online retailers. In January 2025 Klevu joined Searchspring and Intelligent Reach to form Athos Commerce, which now offers ecommerce search, merchandising and personalization. Key capabilities of Klevu AI ecommerce search: self-learning product search Product discovery: helps shoppers reach relevant items Merchandising: controls over ranking and display Personalization: tailors results to shoppers Onsite and external channels: connects shoppers to products across channels Self-learning engine: adapts from shopper behavior How Klevu works A retailer connects its catalog and storefront to the search engine, which learns from shopper behavior to rank results. Merchandisers adjust ranking and display, and personalization tailors results. Athos Commerce now sells the combined capabilities of Searchspring, Klevu and Intelligent Reach. Who uses Klevu? Mid-market online retailers use it to improve onsite search and discovery. Customers and former customers can reach dedicated channels through the Athos Commerce site, and agencies use the partner portal. Klevu pricing After the merger, pricing is custom quoted through Athos sales and standalone Klevu tiers are no longer public. A third-party report puts pre-merger plans at about $449 to $499 a month for up to 50,000 SKUs and 5 store views. Klevu alternatives Alternatives include Nosto for personalization, Bloomreach for search and commerce experience, Syte for visual search, Lily AI for product attributes, and Rep AI for conversational shopping.
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Saaskart Market Grid™
Explore how leading E-commerce AI solutions compare based on customer satisfaction, market presence, adoption, and buyer feedback. The Market Grid helps you identify category leaders, high-performing solutions, and emerging products within the E-commerce AI ecosystem.
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See where e-commerce ai fits in a complete stack, with the other software, AI agents and services each business needs.
What is Revery.ai? Revery.ai is a virtual dressing room AI agent offering an AI virtual dressing room that shows garments on models for online fashion stores. Revery.ai helps online fashion retailers automate virtual dressing room work and get results faster. Key capabilities of Revery.ai Virtual dressing room Outfit combinations Model diversity Shopify integration Catalog-scale processing Conversion analytics How Revery.ai works Revery.ai takes image as input and produces image. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Shopify, Salesforce Commerce Cloud, Magento and BigCommerce, so the agent works inside existing workflows. Who uses Revery.ai? Revery.ai is built for online fashion retailers. It suits teams that want virtual dressing room and outfit combinations without adding headcount, while keeping people in control of review and final decisions. Revery.ai vs Veesual Revery.ai is often compared with Veesual. Revery.ai stands out for virtual dressing room and model diversity. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Profitero? Profitero is a digital shelf analytics AI agent offering digital shelf analytics and AI that help brands win share across online retailers. Founded in 2010 and based in Boston, Massachusetts, USA, Profitero helps global consumer brands automate digital shelf analytics work and get results faster. Key capabilities of Profitero Availability and price tracking Content and search share AI recommendations Retail media insights Marketplace data coverage Automated recommendations How Profitero works Profitero takes retail data as input and produces insights and recommendations. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Amazon, Walmart Marketplace, Shopify and Target Plus, so the agent works inside existing workflows. Who uses Profitero? Profitero is built for global consumer brands. It suits teams that want availability and price tracking and content and search share without adding headcount, while keeping people in control of review and final decisions. Profitero vs Stackline Profitero is often compared with Stackline. Profitero stands out for availability and price tracking and AI recommendations. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is invent.ai? invent.ai is an AI-powered retail planning and decisioning platform for demand forecasting, inventory optimization and pricing strategy. Its conversational agent, Remi, answers retail planning questions. Key capabilities of invent.ai Remi AI agent: Conversational interface for planning decisions Demand forecasting: Adapts in real time at store by SKU level Inventory optimization: Allocation and replenishment Pricing management: Dynamic pricing, promotions and markdowns Assortment and buy planning: Optimizes assortments and buys Multi-agent architecture: Analyzes demand, inventory and pricing together How invent.ai works The platform monitors real-time signals across the retail network at store and SKU level. AI agents produce recommendations with explanations, and users approve or adjust them before automation executes the decisions. Who uses invent.ai? Retailers and brands. Named customers include Alo Yoga, Tailored Brands, Tecovas, Academy Sports + Outdoors, Mavi, West Marine, Five Below and GNC. invent.ai pricing invent.ai does not publish pricing. Reported customer results include an 8 to 11 percent revenue increase within 90 days and a 6 to 8 percent gross margin improvement, which are vendor claims. invent.ai alternatives Alternatives include Blue Yonder for supply chain planning, o9 Solutions for integrated planning, and Relex for retail forecasting and replenishment.
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What is Daydream? Daydream is a fashion shopping AI agent offering an AI shopping agent that helps people find fashion across thousands of brands through conversation. Founded in 2023 and based in San Francisco, California, USA, Daydream helps online fashion shoppers automate AI fashion shopping work and get results faster. Key capabilities of Daydream Conversational search Style understanding Cross-brand results Outfit ideas Real-time personalization Merchandising insights How Daydream works Daydream takes text and image as input and produces text and product results. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Shopify, Salesforce Commerce Cloud, Magento and BigCommerce, so the agent works inside existing workflows. Who uses Daydream? Daydream is built for online fashion shoppers. It suits teams that want conversational search and style understanding without adding headcount, while keeping people in control of review and final decisions. Daydream vs Perplexity Shopping Daydream is often compared with Perplexity Shopping. Daydream stands out for conversational search and cross-brand results. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Particl? Particl is an AI-powered competitor tracking platform that aggregates product revenue, pricing, promotion and assortment data. It covers more than 20,000 retailers. Key capabilities of Particl Competitor research: Top sellers, pricing and inventory down to SKU level. Product research: Real-time data on top products and trends. Assortment analysis: Finds product overlap and where brands invest. Benchmarking and white space: Compares products and finds gaps in colors and categories. Promotions and events: Monitors competitor social channels and promo impact. AI client access: Query data from Claude, ChatGPT or MCP clients. How Particl works Particl aggregates sales and pricing signals across retailers, then lets teams explore competitor sellers, assortments and promotions. Natural-language questions can be asked through Claude, ChatGPT or other MCP clients. Teams use the findings to decide launches, pricing and promotions. Who uses Particl? Particl is used by ecommerce and merchandising teams. The vendor reports 10,000+ brands, including Lululemon, SKIMS, Vuori, Gymshark, Mejuri, Stanley and Chubbies. Particl pricing Particl offers a free trial and paid plans. Specific prices require signup or a demo request and are not listed on the homepage. Particl alternatives Alternatives include Similarweb, which offers web traffic intelligence, Jungle Scout, which targets Amazon sellers, and Helium 10, which supports marketplace product research.
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What is Shopify Sidekick? Shopify Sidekick is an AI-enabled commerce assistant built into the Shopify admin to help merchants start, run and grow a business. Shopify describes it as a launch team in one tool. Key capabilities of Shopify Sidekick Designer: Creates custom store designs from your ideas. Photo Editor: Turns regular photos into product images. Writer: Generates SEO-optimized product descriptions and marketing copy. Tech Support: Handles domain setup and store configuration. Marketer: Creates social content and campaigns. Store operations: Helps with inventory, pricing analysis, email campaigns and shipping setup. Multilingual: Adapts to the store language settings. How Shopify Sidekick works Merchants open Sidekick from the purple glasses icon in the Shopify admin and describe a task in plain language. It works through five personas, from designer to marketer. It respects staff permissions, so team members only reach data they are authorized to view. Who uses Shopify Sidekick? Sidekick is used by Shopify merchants, from new entrepreneurs setting up a first store to teams managing day-to-day operations. Shopify Sidekick pricing Sidekick is included with your Shopify plan at no additional cost. Feature availability and usage limits vary by plan tier. Shopify Sidekick alternatives Alternatives include Shopify Magic features, which cover content generation in the same admin, Jasper, which is a general marketing writing tool, and Gorgias, which focuses on e-commerce support.
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What is Constructor? Constructor is an AI-powered ecommerce search and product discovery platform. Its Commerce Reasoning Engine interprets shopper behavior, context and intent to personalize results. Key capabilities of Constructor AI shopping agents: Natural-language product discovery. Search and autosuggest: Behavior-driven search results. Browse and recommendations: Personalized category pages and suggestions. Retail media: Sponsored product placement. Merchant intelligence: Tools to understand and optimize rankings. Cross-channel discovery: Email, SMS, mobile and in-store experiences. How Constructor works Constructor ingests a retailer's catalog and shopper behavior through an API-first integration, then ranks search, browse and recommendation results with its engine. Merchandisers use Merchant Intelligence to review and adjust ranking. The vendor cites a typical implementation of 8 weeks. Who uses Constructor? Constructor serves large B2C and B2B retailers in fashion, grocery and furniture, with Sephora, Under Armour, Gap, REI, Target and Petco listed. The vendor reports 98.5% client retention. Constructor pricing Constructor does not publish pricing on its website. Retailers request a demo and receive a quote. Constructor alternatives Alternatives include Algolia, which offers developer-focused search APIs, Bloomreach, which combines search with a commerce experience platform, and Coveo, which provides AI search for commerce and service.
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What is Big Sur AI? Big Sur AI is a commerce-first AI company whose AI Sales Agent is tailored to each merchant. It helps shoppers discover products and make informed purchase decisions on an online store. Key capabilities of Big Sur AI Conversational product discovery: shoppers ask questions in natural language Smart search: AI-driven search across the catalog Adaptive quiz: guides shoppers toward the right product Product recommendations: suggests the most relevant products on desktop and mobile Merchant-specific tuning: agent customized for each store Shopify app: installs through the Shopify app store How Big Sur AI works The AI Sales Agent is trained on a merchant's catalog and store content and chats with visitors on desktop and mobile. It answers questions, runs an adaptive quiz and recommends products, with conversations tuned to raise conversion and order size. Who uses Big Sur AI? Big Sur AI is built for e-commerce merchants of all sizes. The company was founded in 2023 by former Google executives Vinod Ramachandran and Arnaud Weber and announced a $6.9 million seed round in March 2024. Big Sur AI pricing The Shopify app is listed as free to install, with additional charges that may apply. Beyond that, Big Sur AI does not publish plan prices in the sources reviewed. Big Sur AI alternatives Alternatives include Rep AI and Zipchat AI for conversational commerce, and Alby and Manifest AI for storefront assistants.
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What is Botika? Botika is an AI platform that generates professional fashion photography featuring artificial models. It helps fashion brands create on-brand imagery at scale for product pages and campaigns, and the vendor now operates at botika.com. Key capabilities of Botika On-model photography: AI models wearing your garments Flat lay images: styled product shots without models Mannequin photography: alternative display option Video generation: moving footage for fashion content Model gallery: models varying in gender, ethnicity, body type and style Retouching workflows: quality assurance on generated images How Botika works You upload product images, pick an AI model from the gallery, and Botika generates photoshoot-quality images in minutes rather than weeks. Retouching workflows check the output before it is used. Who uses Botika? Fashion and apparel brands. The vendor lists Forever 21, Perry Ellis, Jordache and Tobi, with reported results of 90% lower production costs at Jordache and a 6-week to 24-hour turnaround at Juan & Me. Botika pricing Botika does not publish plan prices in its homepage content and links to a pricing page. Botika alternatives Flair also generates fashion imagery, and Nosto, Bloomreach, Vue.ai and Constructor are ecommerce personalization and search platforms rather than image generators.
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E-commerce AI applies machine learning and generative models across online retail, personalization and recommendations, product content, search and merchandising, customer support, and demand forecasting, to grow conversion and efficiency. This guide explains what it is, how it works, what matters, and how to choose one.
E-commerce AI applies machine learning and generative models across online retail, personalization and recommendations, product content, search and merchandising, customer support, and demand forecasting, to grow conversion and efficiency. This guide explains what it is, how it works, what matters, and how to choose one.
E-commerce AI covers tools that optimize online selling: product recommendations and personalization, generative product descriptions and imagery, AI site search and merchandising, customer-support chatbots, pricing optimization, and demand forecasting.
It appears both as standalone tools (recommendation engines, AI search, product-content generators) and as AI features inside e-commerce platforms and marketing suites.
The category centers on conversion, efficiency, and customer experience. Buyers weigh measurable lift in conversion and revenue, data and catalog quality, integration with their store platform, and content quality and brand fit.
E-commerce AI analyzes shopper behavior and catalog data to personalize recommendations and content, powers semantic site search and merchandising, automates support, and forecasts demand, acting in real time on the storefront and behind the scenes.
Platforms combine behavioral and catalog data, recommendation and personalization models, generative content, AI search, and forecasting, integrated with the e-commerce platform and product feed.
Merchants connect their store and catalog, configure personalization, search, and content, and measure conversion and revenue impact, refining over time.
Tailor product recommendations and experiences to each shopper to lift conversion and AOV.
Generate product descriptions, titles, and imagery at scale for large catalogs.
Semantic search and automated merchandising surface the right products faster.
Chatbots resolve order, shipping, and product questions and recover carts 24/7.
Optimize pricing and forecast demand to improve margin and inventory decisions.
Integrate with your e-commerce platform and product feed for real-time action.
Personalization and recommendations lift conversion and average order value.
Generate descriptions and imagery for large catalogs in a fraction of the time.
AI search and merchandising help shoppers find products and buy faster.
Automated support deflects routine inquiries and recovers carts 24/7.
Forecasting and pricing optimization improve margin and reduce stockouts.
| Type | Best for | Ideal size | Pros | Limitations |
|---|---|---|---|---|
| Personalization & recommendations | 1:1 product recommendations | SMB to enterprise | Lifts conversion and AOV | Needs behavioral data |
| Product content AI | Descriptions, titles, imagery | Any | Scales catalog content | Review for accuracy/brand |
| AI site search & merchandising | Discovery and merchandising | Any | Better findability and conversion | Catalog tuning |
| Support & forecasting AI | Support automation, demand/pricing | Mid-market to enterprise | Efficiency and margin | Data and integration heavy |
Fashion & Apparel: Personalize discovery and generate catalog content at scale.
Consumer Electronics: Power search, recommendations, and product support.
Health & Beauty: Recommend products and automate support and content.
Home & Furniture: Improve discovery and merchandising for large catalogs.
Food & Grocery: Forecast demand and personalize recommendations.
Marketplaces: Scale content, search, and support across many sellers.
Look for evidence of measurable lift in conversion, AOV, or revenue, ideally tested against a control.
Confirm clean integration with your e-commerce platform and product feed.
Assess data requirements, personalization and forecasting need sufficient, clean data.
For generated content, verify quality, accuracy, and brand fit at catalog scale.
Ensure AI improves, not frustrates, the shopper experience (search, support).
Understand revenue-share, usage, or seat pricing and model ROI.
Conversational and agentic shopping experiences are emerging, letting shoppers discover and buy through dialogue.
Real-time, individualized personalization is extending across the entire storefront.
Generative content and imagery are automating catalog production end to end.
Buyers should prioritize measurable conversion impact, platform integration, data quality, and shopper experience.
E-commerce AI applies machine learning and generative models across online retail, personalized product recommendations, generative product descriptions and imagery, AI site search and merchandising, customer-support chatbots, pricing optimization, and demand forecasting. It comes both as standalone tools and as AI features inside e-commerce platforms and marketing suites, aimed at growing conversion, efficiency, and customer experience.
It can, through personalization and recommendations that lift conversion and average order value, better product discovery via AI search, and cart recovery through automated support. Results depend on your data quality, catalog, and traffic. Insist on evidence of measurable lift tested against a control group rather than relying on headline claims.
Yes. Generative AI can produce product descriptions, titles, and even imagery for large catalogs in a fraction of the time, using your product data. Quality and brand fit vary, and output should be reviewed for accuracy, especially specs and claims, but it dramatically accelerates catalog content production.
AI site search understands meaning and intent, so shoppers find relevant products even with imprecise queries, synonyms, or natural language, and it can power smarter merchandising and recommendations. Better findability typically improves conversion. Tuning to your catalog and validating relevance on real queries is important.
Leading tools integrate with major e-commerce platforms and product feeds so personalization, search, content, and support work in real time on your storefront. Integration depth varies by platform and setup, so confirm support for your specific stack before adopting.
Reputable vendors provide encryption, access controls, and compliance, and you must manage customer data privacy and consent (GDPR/CCPA). Confirm whether customer and behavioral data is used to train shared models, and review data handling, personalization relies on sensitive shopper data.
Common models are revenue-share or usage-based (for recommendations and search), per-seat, or add-ons within an e-commerce platform. Estimate your traffic, catalog size, and order volume, and model ROI against expected conversion lift to compare true cost.
Prioritize evidence of conversion or revenue lift (control-tested), integration with your platform and product feed, data and catalog requirements, content quality and brand safety, shopper-experience impact, and pricing tied to ROI. Pilot with clear metrics and a control group before scaling across the store.
