+42%
AI-referred visits to US retail converted better than non-AI traffic in early 2026, a full reversal from a year earlier.
Adobe Digital Insights · Q1 2026 
AI for Adobe Commerce AI for Adobe Commerce, built from your live store data
One conversational interface that helps shoppers find products, compare options, ask detailed questions and check orders, running on first-party data captured in your Adobe Commerce store as it happens.
Commerce AI you can trust.
Shopping Assistant Live
Will the Ravin R18 fit a case sized for the R10?
Product Q&A agent
No, the R18 has a wider limb stance than the R10, so it needs the Ravin XL soft case. Two in stock now fit it.
Answer from approved product copy Ask about fit, orders, returns…
Why now
Shoppers are arriving from AI, and your store has to answer them
Shoppers research and compare inside ChatGPT, Gemini, Perplexity and Google AI, then arrive ready to buy, and that traffic now converts better than any other channel.
The harder problem sits on the storefront itself. Keyword search and static product pages were built for shoppers who already know what to type. High-intent shoppers have to ask questions to reach the right product, and when the store cannot answer, they leave.
An Adobe Commerce store that answers questions in the moment, on accurate live data, holds onto more of the traffic it already pays for.
$234B
Annual cost of search abandonment to US retailers, more than $2 trillion globally.
Google Cloud & Harris Poll ~70%
Average cart abandonment across retail, much of it questions a store never answered.
Baymard Institute The platform
An Agentic Commerce OS for Adobe Commerce
Webscale AI is the go-to-market brand for Webscale's Commerce AI line, built for Adobe Commerce and Magento Open Source merchants. The platform is an operating layer that connects first-party commerce data, AI intelligence and real-time storefront execution inside one architecture.
CDP
Captures first-party behavioral data at the infrastructure layer, in real time. The foundation everything else runs on.
Explore CDPAI Segmentation
A plain-English interface that turns that data into deployable audiences, activated into Klaviyo or Magento without an analyst queue.
Explore AI SegmentationAI Shopping Assistant
A single conversational interface on the storefront for discovery, comparison, product questions and order support, plus search that powers your search bar and collection pages.
Explore the AssistantThree products sit on that architecture, and a merchant can start with any one of them. Each delivers value on its own, the value compounds when they run together, because they share one live data layer instead of stitching exports between separate tools.
AI Shopping Assistant
One interface that sells and services your shoppers
The AI Shopping Assistant is a single visitor-facing conversation on your Adobe Commerce storefront. In one thread a shopper can find and compare products, ask detailed questions about fit and compatibility, check an order, start a return and get answers to policy questions. Shopping and service happen in the same place, and the shopper never sees the seams.
Orchestration · live
“Is this scope compatible with my crossbow, and can I still return the one I bought last week?”
Orchestrator
Product Q&A
Order Management
Product Discovery
Customer Support
A single compound question can pass through several agents and come back as one answer. That hidden routing is what separates the interface from a scripted widget.
| Agent | What it handles |
|---|---|
| Product Discovery | Natural-language and visual search, product ranking |
| Product Q&A | Specifications, fit, compatibility, side-by-side comparison |
| Order Management | Status, tracking, returns and RMA flows |
| Customer Support | Policy questions, loyalty, knowledge base answers, handoff to a live agent |
You control how it behaves
You decide how the assistant behaves. Custom workflows trigger on keywords, so a complaint flow can collect contact details and open a ticket in your help desk, or a promotion can switch on for a sale weekend and off after. Every agent accepts a merchant-specific instruction block for brand voice, category terminology and business rules, for example, a required disclaimer on regulated products.
Product answers point back to the exact line of approved product copy that supports them, which keeps the assistant grounded and suppresses invented answers.
Chatbot or assistant
How an AI shopping assistant differs from a chatbot
A rule-based chatbot matches a shopper's message to a script, answers from a fixed decision tree or FAQ and escalates when it cannot find a match. An AI Shopping Assistant reads live store data, understands what the shopper means, remembers the thread, compares products and completes discovery and service tasks in the same conversation. The difference is architectural, and it shows up in what the assistant can actually do.
| Capability | Rule-based chatbot | AI Shopping Assistant |
|---|---|---|
| Live inventory and pricing | No | From your live store data |
| Understands intent | Keyword matching | Natural-language understanding |
| Conversation memory | None | Full thread context |
| Product comparison | No | Yes |
| Order status and returns | Deflect or escalate | Handled in the conversation |
| Grounded answers | Static FAQ | Tied to approved product copy |
On Adobe Commerce
How Webscale AI runs on your Adobe Commerce store
Webscale has run in the traffic path of commerce stores since 2013, which is why the assistant reads live data instead of a nightly export. On Adobe Commerce and Magento Open Source, it connects to your catalog, inventory, pricing and customer accounts, so answers reflect what is true right now, including account-specific contract pricing for B2B buyers.
The assistant also connects into the tools you already run. It reads context from and pushes actions to your help desk, your email platform and your catalog data, so a shopper gets one continuous conversation while the agents work across your connected systems. For customer service, it handles pre-purchase and order questions on the storefront and hands off to your existing help desk for agent-side support. You keep your help desk. The assistant sits in front of it.
Connects with 
Klaviyo
Klaviyo 80% · ~30%
Common service issues AI is expected to resolve on its own by 2029, cutting operating cost about 30%.
Gartner $0.62 vs $7.40
Cost of an AI-handled resolution against a human agent handling the same request.
McKinsey Accurate before it goes live
The onboarding team runs structured interviews with your subject matter experts, turns what they know into training prompts and validates the assistant's answers against real questions before launch. The assistant answers only from what you index: your catalog, approved PDFs, specific URLs and connected systems. It does not go live until both teams agree it answers correctly.
Constrained context is the point. It is what makes the answers predictable.
Why Webscale AI
What makes this different
01
Built from your live store data
Because Webscale sits in the traffic path, first-party behavioral data is captured at the source, in real time, for every session. An AI wrapper on a hosted model has a product feed and a guess. The assistant has your live store.
02
Answers scoped to what you approve
The assistant responds only from data you explicitly index, and every product answer traces back to the copy that supports it. Validated before launch, it behaves the same way in production as it did in review. Predictable answers are the feature.
03
You own the orchestration
The orchestrator routes each request to the right agent, and you control which agents run, what each one knows and how it should behave. Custom workflows let you add store-specific logic without engineering work.
04
Specialist agents behind one interface
Product Discovery, Product Q&A, Order Management and Customer Support run through one implementation on one data layer. One contract, one onboarding, one support relationship, and agents are modular, so you enable the ones your store needs.
Who it's for
Built for stores where a wrong answer costs the most
The stores that gain the most run deep catalogs in categories where shoppers have to ask questions to buy correctly. Adobe Commerce and Magento are common in exactly those categories.
Regulated & restricted commerce
Firearms, cannabis, alcohol, tobacco and regulated medical. Age-gating, jurisdictional rules and required disclaimers, applied in the conversation. A wrong answer here is liability, so the assistant is scoped to approved language and validated before launch.
Regulated IndustriesB2B & distribution
Large SKU catalogs, customer-specific contract pricing, quoting and account context. The assistant knows who is logged in and prices and answers accordingly.
B2B commerceHigh-consideration specialty
Technical fitment, accessory compatibility, variants and seasonal demand. Discovery works by conversation when keyword search falls short.
Product Discovery Agentic Commerce Readiness
Ready for the AI channels your shoppers are already using
Shoppers increasingly discover and buy through AI surfaces, and those surfaces read structured product data rather than browsing your pages. Product data that is incomplete or stale is invisible to the channel growing fastest.
66%
Adobe AI Visibility · Q1 2026
Today the average retail product page is only about 66% machine-readable to AI, the worst-performing page type on retail sites. The open standards for the AI channel are already here, and every one of them needs the same thing from a merchant: accurate, real-time, structured product and account data.
MCP
Model Context Protocol
Anthropic
The data-access layer that lets AI agents read your store.
UCP
Universal Commerce Protocol
Google & Shopify
Defines how agents discover products and check out.
ACP
Agentic Commerce Protocol
OpenAI & Stripe
Discovery and checkout for agent-driven purchases.
AP2
Agent Payments Protocol
Emerging standard
Handles authorization when an agent pays on a shopper's behalf.
That is what the CDP already captures. Because the data lives at the infrastructure layer and updates in real time, an Adobe Commerce store built on Webscale is positioned to present clean, current data to AI agents as these channels mature. Read the Agentic Commerce Report →
FAQ
Adobe Commerce AI, common questions
Does Adobe Commerce have built-in AI?
Adobe Commerce includes native AI features such as Live Search and product recommendations powered by Adobe Sensei. Webscale AI adds a conversational layer on top: an AI Shopping Assistant with specialist agents for discovery, product questions, orders and support, all running on your live store data.
Is an AI shopping assistant the same as a chatbot?
No. A chatbot follows a fixed script and escalates when it cannot match a question. An AI Shopping Assistant reads live inventory and pricing, understands intent, remembers the conversation and completes discovery and service tasks. The difference is the architecture underneath.
Can AI handle customer service on my Adobe Commerce store?
Yes. The assistant answers order-status, returns and policy questions in the same conversation where it helps shoppers buy, and it hands off to your existing help desk for agent-side support. You keep your help desk and connect the assistant to it.
Does it work with Magento Open Source?
Yes. Webscale AI supports Adobe Commerce and Magento Open Source. AI deployments run on Magento and Adobe Commerce today.
Will the AI give wrong answers?
The assistant answers only from data you approve and index, and each product answer traces back to the copy that supports it. The onboarding team validates answers against real questions before the assistant goes live.
Does it replace my search, recommendations and chat tools?
One infrastructure-native layer covers product discovery, recommendations and storefront chat on a single data foundation, which can replace separately bought tools for those jobs. It connects to, rather than replaces, your help desk and email platform.
Is my store ready to sell through ChatGPT and Google AI?
Getting found in AI channels depends on accurate, real-time, structured product data. Webscale captures that data at the infrastructure layer, which is the foundation these emerging standards (MCP, UCP, ACP) require. Readiness for a specific channel depends on how each standard rolls out.
How long does it take to launch?
Timing depends on catalog size and the depth of expert knowledge to capture. The onboarding team runs structured interviews with your subject matter experts and validates the assistant before launch, so it goes live only when both teams agree the answers are right.
Get started
See Webscale AI on your Adobe Commerce store
Bring your catalog and your hardest shopper questions. We will show you the assistant answering them on live data.
Commerce AI you can trust.