Part one
Forward Deployed Engineers
Commerce and data experts who work inside your team to find the missing third wherever it lives and map it in.
AuditCleanupMapping
Alongside your team, every phase Why Webscale AI
Agentic transformation starts with structured data
Webscale AI gets your commerce data ready for AI agents with the Commerce Context Engine, then puts it to work on your storefront, in AI chat ads and wherever shoppers now check out.
The shift
Commerce has done this before
Digital transformation moved the store online. Agentic transformation moves the store into the conversation.
Digital transformation
Agentic transformation A search engine or your homepageWhere shoppers startA question to an AI assistant
Keyword search and filtersHow they find productsAsking, comparing and deciding in conversation
The shopperWho does the browsingThe shopper and an AI agent acting for them
Page design and SEOWhat wins visibilityStructured data context from every source, kept live and accessible to outside agents
Your cartWhere checkout happensWherever the conversation happens
The merchants who treated digital as a website project fell behind the ones who treated it as a new way of doing business.
The same test is here again.
Checkout everywhere
Where can shoppers buy your products now?
Storefronts and marketplaces are no longer the only places a shopper can buy.
Your storefront
Shoppers browse and buy on your own site. Add the AI Shopping Assistant and they can ask, compare and buy in one conversation.
AI Shopping AssistantOn your site
Which of these is best for rocky trails?
Recommended for you
Ridge GTX Trail RunnerSize 9 in stock · Ships tomorrow
Add to cartMarketplaces
Your products listed on someone else’s site.
AI assistants
Checkout inside ChatGPT, Gemini, Perplexity and Copilot, or an agent checks out on your site.
ChatGPT
Gemini
Perplexity
CopilotAI chat ads
Sponsored answers inside AI chat. Ask a question in the ad, then buy in a few taps.
ChatGPT
Google AI ModeMicrosoft Copilot
AI ModeBest waterproof trail runners under $150?
Sponsored
Ridge GTX Trail Runner$138 · In stock · Ships tomorrow
BuyAsk about sizing, returns or fit…
Publishers
Buy the product while reading its review.
Publisher examples
BuzzFeedReaders buy featured products directly inside BuzzFeed articles.
The Good Face ProjectShoppers buy clean-beauty products on the site instead of being redirected to the brand.
Embedded storefronts
Your store placed inside another app or site.
Checkout follows the shopper now.
Every one of these channels reads the same product, price and inventory data.
The real blocker
Broken data blocks every agentic channel
Enterprise commerce data was built for web pages, not for AI agents. If an agent sees the wrong price or a sold-out variant, it loses the sale and learns to skip you.
66%Readable to AI
What the product page shows
The missing third: spec sheets, back-office systems and your experts
The average retail product page is only about 66% readable to AI. The missing third lives in spec sheets, back-office systems and your experts, so software that scrapes your site cannot recover it.
Adobe Digital Insights AI Visibility, Q1 2026
The solution
Two parts, working together: people who find the missing third, and an engine that keeps it live for every agent.
Part two
Commerce Context Engine
Keeps that data complete, live and model-ready for every agent, and writes orders and returns back to your systems.
CompleteLiveModel-ready
How the CCE works Vendor-reported figures
What the market data shows
01 · Returns
43%
of consumers returned a product in the past year because pre-purchase information was wrong.
Vendor reported. Akeneo, 2025 Consumer Returns Report
02 · Conversion
59%
faster sales growth for retailers running their own shopper agents, 6.2% against 3.9%.
Vendor reported. Salesforce, 2025 Holiday Shopping data
03 · AI chat
76%
higher conversion rate for lower-funnel ads inside Microsoft Copilot than traditional search.
Vendor reported. Microsoft Advertising, Copilot ad results
The engagement
What does an agentic transformation look like?
It starts before any connection. Four phases make up the pilot. The same data layer then extends to AI chat ads and off-site checkout.
Start with a pilot
Then scale
00Discover
Learn your brand voice, interview experts and gather knowledge docs.
01Connect the CCE
A no-code install. The CCE starts pulling in data.
02Normalize data
Engineers normalize and validate your data.
03Launch on site
Your AI Shopping Assistant goes live.
04AI chat ads
Ads inside AI chat that report orders. Coming soon
05Sell everywhere
Checkout in AI assistants, publishers and marketplaces.
Forward Deployed Engineers alongside your team, every phaseYour stack stays. One commerce context alongside it, with no code and no replatforming.
Our right to win
Why this is Webscale’s work to lead
Webscale has run commerce infrastructure and data analytics in the traffic path since 2013. Agentic commerce is a data problem first, and data is where we started.
2013In the traffic path since
In the traffic path since 2013
Data
First-party behavior on every session
Captured where your traffic runs, and read by the Commerce Context Engine alongside every other source.
People
Forward Deployed Engineers
Commerce data experts who do the integration and normalization work with your team.
Partner
See the integration Webeyez
Experience analytics that flag lost revenue on the storefront and trigger the assistant.
Questions
Questions we hear
Agentic transformation is the shift that follows digital transformation. Shoppers start with a question to an AI assistant, an AI agent may browse and check out for them, and checkout happens wherever the conversation is. It depends on structured, live commerce data that outside agents can read.
Native assistants only see their own catalog. The Commerce Context Engine connects every source: PIM, ERP, CDP, reviews and your help desk.
No. Webscale AI works with Adobe Commerce, Magento, Shopify, Shopware, Salesforce Commerce Cloud and BigCommerce, on the stack you run today.
It holds a real conversation instead of following a script. One assistant handles discovery, recommendations, orders and support tickets.
It depends on your data. We launch a focused first version, then phase in more. A small merchant with a messy catalog took about eight weeks of data work.
Yes. You set the voice, the guardrails and the data sources, and you can A/B test assistants against each other.
You do. Your raw data stays yours, and it is de-identified before it reaches any language model.
We agree the metrics before launch, then compare before and after on conversion rate, return rate, ad return and new revenue.
Last updated
Next step
Start on your own storefront
Launch the AI Shopping Assistant on the store you already run. The same connection is what reaches every other channel when you are ready.