For E-commerce
A storefront built to sell, and to be bought by the agents doing the shopping
For store owners launching a new store or replacing one that loads slowly, fails on mobile, or was never built with machine-readable product data, who need a storefront that converts a human and qualifies for an AI shopping agent at the same time.
Every engagement is directed by a technical specialist and reviewed before delivery.
What this is
An Ecommerce Store Build is a scoped, done-for-you storefront engineered to do two jobs at once: convert the person who lands on it, and be understood and bought by the AI shopping agents now browsing and checking out on a customer's behalf. We build to Google's Core Web Vitals thresholds at the 75th percentile of real users, WCAG 2.2 AA accessibility, complete Product and Offer schema, and a checkout shaped by a decade of documented usability research, not a generic template that serves neither audience well. Roughly 62 percent of e-commerce traffic is mobile, and only about 42 percent of mobile sites currently pass Core Web Vitals; a store this system produces is built for the phone your buyers actually carry, not the desktop the build was reviewed on.
The problem
Why ecommerce stores lose here
Most store builds are judged on launch-day appearance. Then the real costs show up: pages that load slowly on a phone, a checkout that leaks customers between the cart and the confirmation screen, and product data buried inside marketing copy where no engine can read it. The store works, technically, and still underperforms against both audiences it needs to win.
The ground has shifted under new builds specifically. Morgan Stanley projects agentic commerce could capture 10 to 20 percent of US e-commerce spend by 2030, and OpenAI's own 2026 shopping rollout stumbled on exactly the kind of unstructured, unclear product data a rushed or template-first build tends to produce. A new store launched without machine-readable schema, clean identifiers, and a feed built to spec is choosing to retrofit that work later, under pressure, rather than build it in from day one.
Meanwhile the same fundamentals still decide whether a human buys: does it load fast, does it work on mobile and with a screen reader, and is the checkout short enough to survive the roughly 70 percent average abandonment rate documented across a decade of checkout research. Most platform templates and cheap builds optimize for neither the human nor the machine well, and a bespoke build often quotes a blind number before anyone has looked at what is actually being sold.
The evidence
What the numbers show
Mobile accounts for roughly 62% of e-commerce traffic, while only about 42% of mobile sites currently pass all three Core Web Vitals thresholds, versus roughly 63% on desktop.
emerging Industry benchmark aggregations against Google's published Core Web Vitals standard, web.dev, 2024 to 2026.
The average documented cart abandonment rate is 70.22%, and the typical large e-commerce site could gain roughly 35.26% more completed orders through better checkout design alone.
established Baymard Institute, Cart Abandonment Rate Statistics, 2026.
A well-designed checkout can use as few as 12 to 14 form elements, versus the 20-plus typical on unaudited sites.
established Baymard Institute, Checkout Usability research program.
Agentic commerce could represent $190 to $385 billion, or 10 to 20%, of US e-commerce spend by 2030.
established Morgan Stanley, Agentic Commerce Market Impact Outlook, 2026.
OpenAI's own 2026 shopping rollout pulled back from in-chat checkout, citing inaccurate product data and merchant onboarding difficulty, evidence a new build needs clean structured data from day one.
established OpenAI developer announcements, 2026-02 and 2026-03; CNBC, 2026-03-24.
How it works
The work, made checkable
- 01
Scope against your catalog and your Machine-Readiness Score
We start from what you actually sell, your platform of choice, and a read of where a comparable store in your category currently stands, so the build is scoped to your real starting position, not a generic template.
- 02
Build to the Core Web Vitals thresholds engines actually grade
Largest Contentful Paint at or under 2.5 seconds, Interaction to Next Paint at or under 200 milliseconds, and Cumulative Layout Shift at or under 0.1, measured at the 75th percentile of real users on the Chrome User Experience Report, the same field dataset Google grades against, not a one-off lab score.
- 03
Engineer machine-readable product data from the first page
Complete Product and Offer schema, clean GTIN and attribute fields, and a Merchant Center feed built to specification from launch, so the catalog qualifies for Google's product graph and AI shopping surfaces without a retrofit.
- 04
Shape checkout against a decade of documented research
A checkout built toward the 12 to 14 form-element shape the largest public checkout usability research corpus describes, with full landed cost disclosed early and a genuine guest-checkout option, directly addressing the two most-cited causes of cart abandonment.
- 05
Build to WCAG 2.2 AA from the start
Accessibility is engineered in, not patched on with an overlay, so the store is usable by every visitor and defensible against an accessibility complaint.
- 06
Confirm the build against real-user data before handoff
We re-measure field performance, schema validity, and feed acceptance before delivery, so the store that launches is confirmed against the same standards engines and shoppers will actually judge it by.
Included
What is delivered
- Scoped storefront build on your chosen platform, Shopify, WooCommerce, BigCommerce, or a custom stack.
- Core Web Vitals engineering to Google's published thresholds, verified against real-user field data.
- Complete Product and Offer schema, GTIN and attribute engineering, and a Merchant Center feed built to specification.
- Checkout built toward the research-documented low-friction shape: early cost disclosure, guest checkout, minimal form fields.
- WCAG 2.2 AA accessibility built in from the start.
- Analytics and tracking wired for accurate measurement from launch.
- A pre-launch confirmation pass against field performance, schema validity, and feed acceptance.
The outcome
What it moves
- A storefront that passes Core Web Vitals at the 75th percentile of real users, not just a lab test, on the mobile connections most of your shoppers actually use.
- Complete, machine-readable Product and Offer schema and a Merchant Center feed built to specification from day one, positioned to qualify for AI shopping surfaces as standards solidify.
- A checkout shaped to the research-documented causes of cart abandonment, full cost disclosed early, genuine guest checkout, a short form, rather than a template that repeats the industry's average leak.
- A site built to WCAG 2.2 AA accessibility from the ground up.
- An owned commerce asset your customers can buy from and an AI agent can qualify, add to cart, and complete a purchase through.
- A build confirmed against field performance and feed-acceptance data before handoff, not launched on assumption.
Straight answers
Questions
Why does a new store build need to think about AI shopping agents at all?
Because the same underlying build decisions, machine-readable schema, a clean feed, accurate product identifiers, determine whether both a search engine and an AI shopping agent can qualify your catalog. Building that in from the start avoids the retrofit most existing stores now need. Morgan Stanley projects agentic commerce could reach 10 to 20 percent of US e-commerce spend by 2030.
Is this a template site or a custom build?
It is a scoped, done-for-you build on the platform that fits your catalog and budget, Shopify, WooCommerce, BigCommerce, or a custom stack, engineered to Core Web Vitals, WCAG 2.2 AA, and machine-readable schema standards rather than a generic theme left as-is. A technical specialist directs and reviews the build before delivery.
Can you guarantee my new store will rank or convert at a specific rate?
No. We build to documented, measurable standards, Core Web Vitals thresholds, checkout usability research, schema completeness, and confirm the build against real-user field data before handoff. We do not promise a specific ranking, conversion rate, or sales figure, because those depend on factors, including your own pricing, product-market fit and marketing, that a build alone cannot control.
How is this priced?
The build is priced to your store, since catalog size, platform, and starting complexity vary widely. We publish the full deliverables here, then agree the exact figure with you in writing after a short scoping conversation, once we understand what you are actually building.
Provenance
Sources
- web.dev, Core Web Vitals thresholds, 2024 to 2026 (established)
- Baymard Institute, Cart Abandonment Rate Statistics and Checkout Usability research program, 2026 (established)
- Morgan Stanley, Agentic Commerce Market Impact Outlook, 2026 (established)
- OpenAI developer announcements, 2026-02 and 2026-03; CNBC, 2026-03-24 (established)
- Industry benchmark aggregations, mobile traffic share and Core Web Vitals pass rate, 2026 (emerging)