A catalogue that AI shopping agents can read, compare and buy from, built on work that helps ordinary search today.
Agent readiness is the technical work that lets AI shopping agents find your products, read the right price and stock, and complete a purchase: structured data, feeds that agree with the site, machine-readable policies and a clean checkout. For Magento, Adobe Commerce, Hyvä and Shopify merchants. Estimated in writing once we have read your pages and feeds.
What we usually find on a first look.
Our feed says one price and the product page says another.
Agents read both. A mismatch looks like a trap, and the offer is skipped or distrusted.
We do not know whether our product pages have structured data at all.
Many themes emit some, incomplete or invalid. Identifiers, availability and shipping are the usual gaps.
Our returns policy is a PDF in the footer.
Agents compare offers on delivery and returns. A policy that is only prose cannot be compared.
Six pieces of plumbing. Most of them help ordinary search today.
Product and Offer with identifiers, price, availability, shipping and returns, validated.
Merchant Center and other feeds generated from the same data as the page.
Caching, indexing and feed schedules checked so what an agent reads is what a buyer pays.
Shipping, returns, warranty and contact details in structured data, on findable pages.
Clean guest checkout, and edge rules that tell agents from attacks.
Not a promise of orders from agents, and not a plug-in.
Read what the store exposes, fix the data at source, mark it up, then monitor the reports.
Send us the store address. A senior person puts a few product pages and your feed through the validators and reads them as an agent would.
Within two working daysMissing identifiers, attributes, shipping and returns data added in Magento or Shopify, not patched into a template.
First weeks, depending on the catalogueStructured data templates and feed generation built on a staging copy, validated against the top products, then checked against the feed.
Second stageDeployed at a time agreed with you, then Search Console and Merchant Center diagnostics read each month.
At go-live, then monthlyThe questions we ask of a store before we estimate the work. Most stores pass some and fail others; very few fail all of them.
Name, images, brand, identifiers, price, currency, availability.
Agents read both. A mismatch looks like a trap.
Shipping and return policy markup, and pages an agent can find.
Address, delivery and payment steps that validate cleanly and finish.
Bot rules tuned so that known agents are let through and scrapers and attackers are not.
Search Console and Merchant Center reports read monthly, so a theme change does not silently break the markup.
We price the work, not the clock.
Agent readiness is catalogue, template and feed engineering, estimated in writing once we have read what the store already exposes. Most of it, the structured data and the feeds, improves ordinary search and shopping listings now, whether or not agents ever buy from you, so the work is not a bet on one version of the future.
A store built by this team, selling worldwide. The figures are the store's own, measured by Google.
Design and Hyvä build for a British dance-shoe maker selling worldwide, with ongoing development and support.
Core Web Vitals, real-user data (Chrome UX Report), mobile, 28 days to 31st October 2026. Marks show Google's thresholds.
Every line of code and every piece of content that AI tooling drafts is read, tested and signed off by a senior engineer.
Years of Magento and Shopify work tell us what is safe to change on a trading store, and what needs a second look.
Whether we do it for you or your own team does it with AI, the same senior person stays answerable for the store.
Some of both. AI assistants already read product pages and feeds to answer shopping questions, and large AI, payment and platform companies are publishing protocols for agents to buy on a person's behalf. Nobody can tell you how many orders will arrive that way, and we will not pretend to. The work we propose is the part that pays now whatever happens.
Not for most stores today. Agents start with what search engines already read: Schema.org Product and Offer data on the page and a product feed that agrees with it. Where a protocol matters for your store and has settled enough to build against, we will say so and estimate it as a separate piece of work.
Some will, which is why the data has to carry what makes you worth choosing: delivery time, returns, stock, warranty, identifiers and the attributes a buyer filters on. A store that exposes only a name and a price will be compared on price.
Yes, with less to build. Shopify emits a good deal of structured data and feed plumbing itself, and is adding agent features of its own; the work there is making sure your product data is complete and true, and that theme changes have not broken what Shopify emits.
No. Nobody controls what an assistant chooses to show, any more than they control Google's rankings. We can make sure that when an agent reads your store it finds complete, consistent, current data, which is the part a merchant can control.
It overlaps. Structured data, feeds and clean attributes are also what search engines and shopping listings use, so most of this work improves ordinary search visibility today. The agent-specific parts are availability and policy data in machine-readable form and a checkout that works without a human improvising.
Give us the address and we will read what the store already shows the world: release, support status, PHP, headers, hosting and any sign of a hijack. One page, by email, within two working days. Nothing is accessed or tested.
What we would do first. Apply the outstanding security fixes, then plan the upgrade to a supported release.