If you’ve shopped online recently, you’ve probably noticed something different. Maybe it was a chat assistant popping up to help you find the perfect gift, or an app that let you snap a photo of a jacket and instantly found similar styles. That’s AI at work, and it’s changing how we shop. Nearly 45% of U.S. consumers now use AI when shopping online, and the global AI shopping assistant market was valued at approximately $4.67 billion in 2024.
From visual search tools that identify products from photos to intelligent assistants that compare options across retailers, AI shopping apps have changed how we find, evaluate, and purchase products. Instead of opening ten browser tabs or scrolling endlessly through search results, these tools can understand what you want and surface relevant options faster.
Visual context is also becoming more important. A screenshot of an outfit, a photo of a chair, or a saved product image can communicate details that would be tedious to explain in a prompt. Personal AI assistants can take that a step further by using the images you choose to share as context for your tastes and preferences.
The challenge? With dozens of AI shopping tools available, finding the right one for your needs requires cutting through marketing hype. We analyzed 100+ AI shopping tools, tested top candidates, and reviewed available product information and case studies to identify 13 options available today.
This guide covers consumer shopping assistants alongside tools designed for ecommerce businesses.
Key Takeaways
- Dazzle brings personal context into shopping. Its “Pictures, Not Prompts” approach uses selected photos and screenshots to understand shopping tastes, style, favorite places, and other preferences that can make recommendations more personal.
- Dazzle can turn shopping images into action. Product screenshots and photos can become useful next steps, including finding products and generating personalized ideas through Dazzle Photo Intelligence.
- Visual search is now a major shopping workflow. Shoppers can increasingly start with an image instead of trying to describe a product in words.
- Some business tools report measurable ROI. In vendor case studies, shoppers who engage with AI shopping assistants have shown higher conversion rates, although results vary by brand, traffic, implementation, and measurement method.
- Cross-retailer discovery is expanding. AI assistants increasingly help shoppers research products across multiple sources rather than relying on a single catalog.
- Autonomous shopping is emerging. Some consumers are becoming comfortable letting AI handle more steps between product discovery and purchase.
Redefining Online Shopping: The Power of AI in Your Pocket
Traditional online shopping involves scrolling, tab switching, and manually translating what you want into search terms. AI shopping apps reduce that friction by interpreting natural language, images, preferences, and other context to help surface relevant products.
These tools use large language models, computer vision, and machine learning to interpret requests such as “birthday gifts for a 10-year-old who loves dinosaurs” or “waterproof hiking boots for rainy weekend trips.” Some can also work directly from product screenshots or photos, removing the need to describe colors, cuts, materials, or styles manually.
How AI Transforms Browsing to Buying
The shift involves moving from basic keyword search toward more contextual shopping assistance:
- Natural language processing interprets conversational requests instead of requiring exact keywords
- Preference awareness can help tailor recommendations around your tastes and past interactions
- Visual recognition identifies products, styles, colors, and other details from photos and screenshots
- Product comparison helps evaluate options across different sources
- Review synthesis condenses large volumes of customer feedback into useful themes
The Core Benefits of AI-Powered Shopping
AI shopping assistants can provide practical help throughout the purchase journey:
- Time savings. Narrow a large product category into a more manageable set of options
- Visual discovery. Start with a photo or screenshot when words are not enough
- Reduced decision fatigue. Use contextual recommendations to filter irrelevant choices
- Personalized gifting. Consider preferences and occasion context when generating ideas
- Product research. Bring specifications, reviews, and comparisons into a conversational workflow
1) Dazzle: Personalized Visual Shopping Assistant
Best For: Shoppers who save products, outfits, gifts, places, and inspiration as photos or screenshots and want those images to become useful personal shopping context
Key Features
- “Pictures, Not Prompts” approach built around photos and screenshots
- Dazzle Photo Intelligence for understanding personal tastes and shopping preferences
- Product finding from screenshots and photographed items
- Personalized recommendations informed by selected visual context
- Access through the Dazzle app, web, text, email summaries, and supported agent-to-agent workflows
Dazzle approaches AI shopping as part of a broader personal assistant experience. Instead of requiring you to repeatedly explain what colors, brands, styles, places, or products you tend to like, Dazzle can use the photos and screenshots you choose to share as personal context.
Its core idea is “Pictures, Not Prompts.” Dazzle Photo Intelligence translates selected images into context that can help the assistant understand your shopping tastes, visual style, favorite places, habits, and preferences. That can be especially useful if your camera roll already acts as an informal shopping list filled with screenshots of clothes, furniture, gifts, recipes, travel ideas, and products you want to remember.
Dazzle can also turn images into action. A saved product screenshot or photo can become a starting point for finding products, generating ideas, or identifying useful next steps. Rather than treating visual search as a one-off lookup, Dazzle connects those images with the broader preferences it has learned from the context you choose to provide.
This makes the shopping experience more personal over time. A gift recommendation can take relevant social context into account, while a product search can reflect styles and tastes already visible in your selected images.
Dazzle is designed as a personal AI assistant and photo-intelligence agent rather than a standalone product-search engine. Shopping is one of several everyday workflows where its visual context can reduce manual input and make recommendations feel more connected to your life.
2) Alexa for Shopping
Relevant For: Shoppers who primarily buy through Amazon and want shopping assistance inside that ecosystem
Key Features
- Generative AI connected with Amazon’s product catalog
- Natural language shopping queries
- Customer review summarization
- Integration with the main Amazon shopping experience
Amazon’s shopping experience uses conversational AI to help shoppers research products, ask questions, and narrow large catalogs.
Rather than relying entirely on keyword searches, shoppers can ask more specific questions about use cases, product characteristics, or differences between options. Review summaries can also condense recurring customer feedback into more manageable takeaways.
The experience centers on Amazon’s shopping ecosystem, although responses may incorporate relevant information from the broader web.
3) Perplexity Shopping
Relevant For: Shoppers who want research-led product discovery across multiple retailers
Key Features
- Cross-retailer product comparison
- “Snap to Shop” visual search from photos
- Supported checkout workflows
- Research-focused interface with cited sources
Perplexity Shopping combines conversational product discovery with the research-focused interface associated with its broader AI search experience.
Its visual search capability lets users start with an image rather than a written description. Photographing a piece of furniture, clothing, or another product can help surface visually related options across retailers.
The research-oriented format is useful when a shopping decision requires more than a simple product result. Users can ask follow-up questions, compare specifications, and investigate differences without starting separate searches for every consideration.
4) ChatGPT Shopping
Relevant For: Users who want product research integrated into a general-purpose AI assistant
Key Features
- Integrations with shopping and service apps
- Shopping Research for detailed product analysis
- Conversational research across product categories
- Voice-enabled shopping queries
ChatGPT brings shopping research into a broader conversational assistant environment. Users can move from an initial request to follow-up comparisons, questions about specifications, and alternative recommendations within the same conversation.
Shopping Research can analyze products across multiple dimensions, such as features, reviews, and other decision criteria. The broader app ecosystem can also connect shopping conversations with supported services.
This format can be particularly useful when the product decision is part of a bigger task, such as planning a dinner, preparing for a trip, furnishing a room, or choosing a gift.
5) Alhena AI
Relevant For: Ecommerce businesses using conversational AI across customer shopping and support workflows
Key Features
- Agentic commerce covering pre-sale through post-purchase interactions
- Deployment across multiple customer channels
- Revenue attribution and analytics
- Product discovery and customer support workflows
Alhena AI combines sales and support functions within an ecommerce-focused AI assistant. Its platform is used for interactions ranging from product discovery to post-purchase questions.
Published customer case studies report conversion improvements and revenue associated with AI-assisted interactions, although outcomes depend on each brand’s implementation and customer base.
For ecommerce teams, the platform’s focus is connecting conversational assistance with measurable customer activity throughout the buying journey.
6) Tidio (Lyro)
Relevant For: Ecommerce businesses that want conversational product assistance and customer support
Key Features
- Shopify product catalog synchronization
- Conversational product recommendations
- Multi-channel customer communication
- AI handling for common customer questions
Tidio’s Lyro assistant connects product information with conversational customer support. Product catalog synchronization helps the system answer questions using current store information without requiring merchants to manually enter every product detail.
Lyro can handle product questions, guide shoppers toward relevant items, and address routine service requests within the same customer conversation.
For smaller ecommerce teams, this can reduce the amount of repetitive product and support information that staff need to communicate manually.
7) Walmart Sparky
Relevant For: Shoppers who frequently buy through Walmart
Key Features
- Conversational product discovery within Walmart’s catalog
- Customer review summarization
- Occasion-based shopping queries
- Order-related assistance and reorder suggestions
Walmart Sparky brings conversational shopping assistance to Walmart’s retail ecosystem. Users can describe what they need in everyday language rather than relying entirely on conventional category or keyword searches.
Review summarization can help shoppers process customer feedback across heavily reviewed products, while occasion-based queries support broader requests such as gifts or items for an event.
The experience is centered on Walmart’s product selection and shopping workflows.
8) Google Gemini Shopping
Relevant For: Users who want product discovery connected with Google’s search and visual technologies
Key Features
- Access to Google’s large Shopping Graph
- Visual product inspiration and search
- Product information from retailers across the web
- Integration with Google’s broader ecosystem
Gemini can draw on Google’s extensive product infrastructure to surface products and shopping information across a broad range of retailers.
Visual search also builds on Google’s long-standing image-recognition capabilities. Users can begin with a product they encounter in the real world or online and search for visually related items.
For people who already use Google services, shopping questions can become part of broader research and planning workflows.
9) Shop.app
Relevant For: Shoppers who buy from brands and stores using Shopify
Key Features
- Product discovery across Shopify merchants
- Natural language shopping queries
- Integrated order tracking for Shopify purchases
- Conversational shopping through supported AI-agent connections
Shop connects shoppers with products from merchants operating through Shopify.
Its discovery experience can help users browse products and brands through conversational queries, while order tracking provides a centralized view of purchases made through participating stores.
Shop also extends into supported AI environments, creating additional ways for shoppers to discover products through conversational interfaces.
10) Gorgias AI
Relevant For: Ecommerce brands connecting customer service with product discovery and sales
Key Features
- Automated handling of routine customer conversations
- Support-to-revenue attribution
- Ecommerce integrations
- Product recommendation capabilities within support interactions
Gorgias uses AI within ecommerce customer-service workflows and can connect support conversations with product discovery or sales opportunities.
Routine questions can be automated while more complex situations remain available for human support. The system can also recognize when product recommendations are relevant within a service conversation.
This approach treats shopping assistance and customer support as connected parts of the same customer journey.
11) Rep AI
Relevant For: Ecommerce businesses focused on engaging shoppers before they leave a site
Key Features
- Intent prediction and proactive engagement
- Cart abandonment workflows
- Behavioral analysis based on visitor activity
- Personalized product recommendations
Rep AI focuses on shopper behavior during the consideration stage.
Its system analyzes visitor activity and can proactively engage when behavior suggests that a shopper may need additional information or assistance. Product recommendations and cart-related conversations can then be adapted to that context.
The emphasis is on using behavioral signals to determine when an AI shopping assistant should engage rather than waiting for every shopper to initiate a conversation.
12) Insider (Agent One)
Relevant For: Large ecommerce organizations deploying AI across multiple customer channels
Key Features
- Separate agents for shopping, support, and insights
- Customer data platform integration
- Deployment across web, messaging, and social channels
- Personalized customer interactions using existing business data
Insider’s Agent One separates shopping, support, and insights functions while connecting them with broader customer data.
That structure enables ecommerce organizations to apply different AI workflows across customer interactions rather than relying on a single generalized assistant for every use case.
Its omnichannel orientation is designed for businesses managing customer conversations across multiple digital touchpoints.
13) ModeSens
Relevant For: Fashion shoppers researching designer products across multiple retailers
Key Features
- Cross-retailer luxury product comparison
- Designer fashion tracking and alerts
- Visual search for finding similar styles
- Product discovery across fashion retailers
ModeSens focuses on designer and luxury shopping, where the same product can appear across multiple stores.
Its comparison tools help shoppers research availability and product options across retailers, while visual search provides another path for finding similar styles when the exact item is unavailable.
The category-specific focus makes it particularly relevant when shoppers already know the designer, item type, or visual style they want to explore.
Beyond Products: AI Shopping for Experiences and Gifts
AI shopping assistance is expanding beyond individual product searches. Personal context can also help with gift selection, experiences, restaurants, travel ideas, and other decisions where preferences matter as much as product specifications.
The Art of Thoughtful Gifting with AI
Modern AI assistants can help:
- Use personal context to generate gift ideas that fit the recipient
- Match occasions with appropriate categories and experiences
- Remember preferences that would otherwise need to be explained repeatedly
- Combine products and experiences when generating ideas
Visual context can make these workflows even more useful. Screenshots of products someone likes, photos from previous trips, saved restaurants, style inspiration, or images related to hobbies can provide clues that are difficult to express through a generic search query.
The final decision still belongs to the shopper, but AI can reduce the research required to get from a vague idea to a useful shortlist.
Privacy and Security in AI Shopping Apps
Using AI shopping apps can involve sharing search queries, preferences, purchase information, browsing behavior, and photos you intentionally provide for visual search or personalization. Understanding how a service handles that information is an important part of choosing how much context to share.
Key Privacy Considerations
- Data security: Review how account information and personal context are protected
- Photo permissions: Understand whether an app analyzes only selected images or requests broader access
- Data retention: Check how long uploaded images, conversations, and shopping information may be retained
- Third-party sharing: Review how personal information can be shared with outside services
- User control: Look for clear options around permissions, account data, and deletion
Visual personalization can be particularly valuable because photos contain rich context, but that also makes user control important. A useful shopping assistant should make it clear what information you are choosing to share and how that context contributes to the experience.
Why Dazzle Brings a More Personal Approach to AI Shopping
Shopping rarely begins with a perfectly written query. More often, you see something you like, take a picture, save a screenshot, photograph a product in a store, or keep an image because you want to remember it later.
That everyday behavior is where Dazzle’s approach becomes particularly relevant.
Instead of making you translate those visual memories into detailed prompts, Dazzle uses the photos and screenshots you choose to share as personal context. Dazzle Photo Intelligence can help your personal agent understand patterns in what you save, including shopping tastes, visual style, favorite places, products, and other preferences.
That creates several useful shopping workflows:
- Save a product screenshot and turn it into a starting point for product discovery
- Use photos and screenshots to give recommendations more personal context
- Get gift ideas informed by relevant tastes and social context
- Move from an image to useful next steps without manually rewriting what is already visible
- Carry preference context into supported assistant and agent workflows
The bigger idea is that shopping is not isolated from the rest of your life. What you buy connects with places you visit, people you shop for, styles you like, trips you’re planning, and screenshots you save for later.
Dazzle brings those signals together through its “Pictures, Not Prompts” approach. Rather than starting every shopping search from zero, your selected visual context can help your personal AI assistant begin with a better understanding of what you already like.
Frequently Asked Questions
How do AI shopping apps personalize recommendations?
AI shopping apps can use information such as search queries, stated preferences, previous interactions, browsing behavior, purchase information, and images you intentionally provide. Different services use different combinations of these signals. More contextual systems may also use information from previous interactions to avoid making you repeatedly explain the same preferences.
Can AI shopping apps truly find better deals?
AI shopping tools can make comparison easier when they search products across multiple retailers or sources. Their usefulness depends on the coverage and freshness of the information available to them. Some tools focus on a single retailer, while others are designed for broader product research. Price tracking can also help shoppers notice changes over time, but it is still worth checking the retailer before purchasing.
What are the privacy considerations when using AI shopping apps?
The amount of information collected varies considerably between services. Depending on the app, this may include search queries, purchase activity, browsing behavior, conversations, or photos you intentionally share. Review the service’s privacy settings and policies to understand permissions, retention, third-party sharing, and deletion controls. For photo-based personalization in particular, look for clear controls over which images the assistant can access.
How do AI shopping apps use my photos or screenshots?
Visual shopping tools analyze characteristics such as product type, color, shape, pattern, style, text, and other visible details. A photograph of furniture, clothing, or another item can therefore become a search input without requiring you to describe everything manually. More personalized assistants can also use selected images as context for understanding preferences. Image processing and retention practices vary by service, so review the relevant privacy controls before sharing personal photos.
How can Dazzle help with shopping?
Dazzle uses a “Pictures, Not Prompts” approach that turns selected photos and screenshots into personal context for its AI assistant. If your camera roll contains saved outfits, products, gifts, restaurants, travel inspiration, or other shopping ideas, Dazzle Photo Intelligence can use that context to better understand your tastes and preferences. It can also help find products from images and turn screenshots into useful next steps, making shopping part of a broader personalized assistant experience rather than requiring you to start every search with a detailed prompt.