Key takeaways
- An AI shopping assistant gives shoppers conversational product guidance grounded in a retailer's data.
- The most useful differences between platforms are channel coverage, workflow depth, pricing, and continuity across the customer journey.
- At Delight.ai, we connect shopping and post-purchase care through persistent memory and one conversation thread across channels.
At a glance
- Alhena AI is best for brands seeking a proactive shopping and support concierge with published conversion proof.
- Gorgias AI Agent is best for ecommerce teams that already use the Gorgias helpdesk.
- Rep AI is best for brands focused on proactive selling, product discovery, and support automation.
- Ringly.io is best for Shopify brands that want an AI phone agent.
- Delight.ai is our recommendation for retailers that want persistent context across shopping and post-purchase care on chat, SMS, email, voice, WhatsApp, and in-app messaging.
Retail teams often evaluate an AI shopping assistant as a storefront chat tool, but the real buying question is what happens when a shopper's needs move beyond that chat window. Can the platform understand who they're talking to, act on live business data, and carry the context into whatever happens next? That's the lens we'll use to compare five current options across capabilities, customer proof, ratings, pricing, and continuity.
What is an AI shopping assistant?
An AI shopping assistant is software that helps shoppers find, compare, and buy products through natural-language conversations. Because it's connected to product, inventory, order, and customer data, it can answer questions about fit, availability, and price, then help the shopper take the next step.
Older store search tools work best when shoppers type the exact product words they're looking for, and basic chat widgets tend to follow a preset script. AI shopping assistants can understand open-ended requests such as "I need a carry-on that will fit under the seat" or "Which of these moisturizers is better for sensitive skin?" What happens next depends on the data and workflows the assistant can access, so it may answer the question, recommend a product, update a cart, check an order, or begin a return.
That range is why "AI shopping assistant" now describes several kinds of software, from on-site product guides to ecommerce helpdesk agents and phone-based assistants. As you compare them, look at the part of the journey each one covers and how much work it can complete once it understands what the shopper needs.
How is an AI shopping assistant different from a chatbot?
An AI shopping assistant understands more of the shopper's intent and can do more with that understanding. Traditional chatbots usually match a question to a scripted answer or help article, while shopping assistants can pull current product or order information and complete approved tasks.
You can see the difference when a shopper is ready to act. A polished answer might help someone choose a product, but the purchase can still stall if the tool can't add the item, apply an eligible discount, or carry the conversation into checkout.
Once an AI shopping assistant can move from answering a question to completing a task, it becomes part of what's often called agentic commerce. That term gives retail teams a more useful way to compare products because the conversation is only the starting point; the real test is how far the assistant can carry the shopper across discovery, purchase, and care before a person needs to step in.
Is an AI shopping assistant the same as a virtual assistant?
No, an AI shopping assistant and a human virtual assistant serve different roles, even though search results often blur the two. A human virtual assistant typically handles scheduled operational work such as catalog maintenance, order entry, customer email, or merchandising support; an AI shopping assistant serves shoppers in real time and can manage many conversations at once.
Many retailers can use both. AI can cover high-volume storefront conversations as they happen, while a human virtual assistant handles work that calls for flexibility, context, or judgment.
What should a retail business evaluate before choosing an AI shopping assistant?
A retail business should evaluate data grounding, channel coverage, human handoff, and post-purchase continuity before choosing an AI shopping assistant. Feature lists can tell you what a product claims to support, but these four questions reveal how it's likely to behave inside your actual operation.
Is the AI shopping assistant grounded in your actual catalog?
Yes, an effective shopping assistant should be grounded in current catalog and operational data. Ask how often the platform synchronizes product details, inventory, pricing, promotions, and order status, then test what it does when that information is missing, conflicting, or stale. A confident answer isn't useful if the product it recommends sold out an hour ago.
Does the AI shopping assistant cover the channels your shoppers already use?
Yes, the assistant should cover the channels your shoppers already use. Confirm support for your mix of web chat, email, SMS, WhatsApp, social messaging, and voice, and pay close attention to what happens when a shopper changes channels. Channel coverage means less if the shopper has to start over each time.
Does the AI shopping assistant provide a clear human handoff?
Yes, the AI shopping assistant should provide an explicit, configurable handoff with rich context. You'll want to know what triggers an escalation, where the conversation lands, and what history or reasoning the human agent receives, because a transfer that drops the context asks the shopper to pay for the system's limitation with their time.
Can the AI shopping assistant help after checkout?
Yes, an AI shopping assistant can help after checkout if its integrations and workflows support post-purchase service. Many current products now cover order tracking, returns, refunds, cancellations, and subscription changes, so the sharper question is whether the shopper's identity, memory, and conversation history survive across those tasks.
What are the best AI shopping assistants, and how do they compare?
The best AI shopping assistant depends on the channels, workflows, and systems your retail team already uses. Alhena AI, Gorgias AI Agent, Rep AI, Ringly.io, and Delight.ai each have a credible place in the market; for retailers that care most about persistent memory and cross-channel continuity, we believe Delight.ai has the strongest fit.
Alhena AI
Best for
Brands seeking a proactive shopping and support concierge with strong published conversion proof.
What it does
Alhena combines shopping and support functions, including product recommendations, product cards, agentic checkout, and human transfer. In its Tatcha deployment, the assistant connects with Salesforce Commerce Cloud and customer-data systems, so the conversation can move from a skin assessment to a prefilled checkout.
Customer proof
Tatcha reported a 3x conversation-to-purchase conversion rate versus its site average, a 38% AOV uplift, and influence on 11.4% of site revenue.
G2 rating
Pricing
A free plan includes 25 conversations per month, and the Essentials plan costs $199 per month with annual billing for 200 conversation credits.
Gorgias AI Agent
Best for
Ecommerce teams that already run customer service through Gorgias.
What it does
Gorgias AI Agent works inside the Gorgias helpdesk, which makes it a natural extension for teams already using that system, and it supports product recommendations, order edits, returns, refunds, and subscription management across connected ecommerce workflows.
Customer proof
Psycho Bunny reported that AI Agent resolved 26% of tickets during its initial deployment and achieved a 4.67 CSAT score during the first 2 months.
G2 rating
4.6 out of 5 from 557 reviews.
Pricing
Gorgias helpdesk plans begin at $10 per month, while AI Agent is a separate add-on that costs $0.90 per fully resolved interaction on most annual plans or $1 on monthly plans.
Rep AI
Best for
Ecommerce brands focused on proactive selling, product discovery, and support automation.
What it does
Rep AI offers sales, support, and combined concierge plans, so ecommerce brands can start with the side of the journey they care about most. Across those plans, the platform supports product recommendations, cart recovery, order status, returns, cancellations, human routing, and additional channels through add-ons.
Customer proof
Rep AI reports that Bikes Online generated 8.4% of revenue through the platform, increased AOV by 48%, and reached a 92.53% AI resolution rate.
G2 rating
Pricing
Plans start at $99 per month and scale with traffic and selected modules, with higher sample pricing published for stores with 80,000 monthly sessions.
Ringly.io
Best for
Shopify brands that want AI phone support for product, order, and return calls.
What it does
Ringly brings the shopping assistant to the phone, where it can identify callers, read Shopify order history, search the product catalog, check stock, request returns, send emails, and transfer unresolved calls.
Customer proof
Ringly's live site shows a product example with a 75% resolution rate and $6,942 in attributed revenue. These figures are vendor-presented interface examples, not a named customer case study.
G2 rating
G2 lists Ringly.io but does not display a score because the product has too few reviews for buying insight.
Pricing
The Grow plan costs $349 per month for 1,000 minutes, while Pro costs $799 per month for 2,500 minutes and includes a 65% resolution guarantee after 90 days.
Delight.ai
Best for
Retailers that want one AI concierge to carry context across shopping and post-purchase care on multiple channels.
What it does
At Delight.ai, we connect Agent Memory Platform, For You Conversations, and Omnipresence so the shopper's context doesn't disappear when the need or channel changes. The same platform supports personalized discovery, proactive cart recovery, order updates, returns, and refunds across chat, SMS, email, voice, WhatsApp, and in-app messaging.
Customer proof
Furniture retailer Hanssem increased its resolution rate from 48% to about 86% over 5 months and reduced transfers to humans by 50%. This is approved Delight.ai retail support evidence and is not presented as a shopping-conversion result.
G2 rating
Sendbird, Delight.ai's parent company, is rated 4.6 out of 5 from 126 reviews on G2.
Pricing
We use conversation-based fees, and Delight Desk has a $0 seat fee. Customers pay for successful AI resolutions, so the helpdesk doesn't add a per-agent charge as the team grows.
Comparison table
The overlap between shopping and support is growing, which is good news for retailers and a reason to look beyond a generic feature checklist. We've built Delight.ai for the team that values persistent memory, cross-channel context, and one operating layer across the relationship, especially when a shopper's next question may arrive on a completely different channel.
Where do AI shopping assistants fall short after checkout?
AI shopping assistants fall short after checkout when their data connections and approved workflows cover only the buying stage, though that boundary is moving because every vendor in this comparison now publishes support for post-purchase tasks.
Coverage alone doesn't guarantee continuity. A platform may be able to process a return and still treat the person requesting it as a stranger, so retailers need to verify whether the same identity, preferences, and conversation history carry across channels and teams.
This is where a realistic test tells you more than a demo. Try a scenario that crosses the purchase boundary, such as a sizing recommendation followed by a delivery update, a return, and a replacement request, and see how much context survives from one step to the next.
How does Delight.ai connect shopping and service?
Delight.ai connects shopping and service by using the same memory, business context, and governance across discovery, purchase, and care. We build that continuity through Agent Memory Platform, which combines customer memory with business intent, and For You Conversations, which adapts each interaction to the shopper's history, preferences, goals, timing, and channel.
That understanding stays useful because Omnipresence carries the context when a shopper moves from chat to a call, then continues the follow-up through SMS or email. We can also reach out after a cart is abandoned or a delivery issue appears, while Interactive Forms collect structured information inside the conversation so the shopper doesn't get bounced to another screen.
For retailers, the result is one connected operating model for AI-led shopping and service, with human teams still in control of escalation rules. When judgment is required, the conversation reaches a person with the context attached, which means the handoff can continue the experience the shopper already started.
Do AI shopping assistants improve conversion?
Yes, AI shopping assistants can improve conversion in specific deployments, but retailers need a clean baseline before the headline number means much.
Alhena reports that Tatcha achieved 3x the site-average conversion rate for assistant conversations, while Rep AI reports a 21.36% conversion rate for engaged shoppers at Vertical Spice.
Both are vendor-published customer results, so they show what's possible in those deployments without establishing a category-wide benchmark.
How should retailers measure conversion lift?
Retailers should measure conversion lift by tracking conversion rate, average order value, assisted revenue, containment or resolution, escalation rate, CSAT, and repeat purchase. You'll also want to define attribution before launch and compare equivalent traffic and time periods, since an assistant-influenced session isn't the same thing as the store-wide average.
The Delight AI Index found that 71% of consumers have used AI customer service, while 57% say they like it, based on 1,001 U.S. respondents. That adoption and satisfaction gap makes experience quality a core part of the business case.
What should retailers take away?
Retailers should take away that choosing an AI shopping assistant means deciding how much of the customer relationship one platform should support. If persistent memory and cross-channel continuity are central to that decision, we believe Delight.ai is the strongest fit in this set. See how we connect carts to care.
