AI agents now browse, compare and check out on a shopper's behalf. We make your Shopify catalogue, feeds and checkout machine-readable so your products get found, cited and bought.
Agentic commerce is the shift from a human browsing your storefront to an AI agent doing it for them. ChatGPT, Perplexity and Google's shopping surfaces already read catalogues, compare options and hand back a shortlist. If your product data is only legible to a browser, you are invisible at exactly the moment the decision is made.
An agent never sees your hero video or your carousel. It reads structured data, product feeds and APIs. That means Product and Offer schema that is complete rather than decorative, attributes an agent can filter on — size, material, compatibility, shipping window — and stock and price that are accurate at request time, not cached from last night.
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The second half is transaction. Shopify's work on agent-facing checkout and the emerging Universal Commerce Protocol lets an agent complete a purchase without a human touching your cart. Agents reach that data over the Model Context Protocol (MCP) — Shopify exposes a storefront MCP endpoint an assistant calls to search your catalogue, read policies and build a cart. We prepare the plumbing: clean product identifiers, agent-readable availability, policies an agent can quote back accurately, and analytics that separate agent traffic from human traffic so you can actually see it happening.
Talk to an engineerThe data formats, protocols and surfaces we use to make Shopify stores readable and transactable by AI agents.
Complete Product, Offer and AggregateRating JSON-LD — the layer an agent parses before it ever renders a pixel of your theme.
Google Merchant Center, Bing and agent-facing catalogue feeds kept in sync with Shopify inventory, price and variant state.
A machine-facing map of what your store sells and what it stands for, so language models summarise you correctly instead of guessing.
Shopify agent-facing checkout and Universal Commerce Protocol readiness — identifiers, availability and policy data an agent can transact on. Customer account extensions cover the post-purchase half.
Separating agent and assistant referrals from ordinary organic traffic, so AI-driven revenue is a number you can report.
Tracking how ChatGPT, Perplexity and Gemini describe your brand and products month over month — and what they get wrong.
Six production-grade capabilities — from structured data and feeds to agent checkout readiness and attribution. Each one shipped, measured, and tied to how AI surfaces actually consume your catalogue.
Structured data coverage, feed health, identifier hygiene, crawler and agent access, and how AI surfaces currently describe you. Output is a prioritized fix list.
Product, Offer, Organization and FAQ schema generated from real Shopify data rather than hardcoded in the theme — so it stays correct as the catalogue moves.
Identifier, availability and policy data prepared for agent-facing checkout and the Universal Commerce Protocol as Shopify rolls it out.
Merchant Center and agent-facing feeds wired to Shopify so price, stock and variants never drift from what an agent is told.
Question-led headings, fact-backed claims and llms.txt — the structure that makes a model cite you rather than paraphrase a competitor.
Agent traffic separated in analytics, citation tracking across major assistants, and reporting that shows what AI surfaces actually send.
No-commitment scoping call · response within 1 business day
How a store prepared for agentic commerce compares to a well-built but agent-blind Shopify storefront.
| Criterion | Agentic-ready | Standard storefront |
|---|---|---|
| Product data | Complete schema generated from Shopify data | Partial schema hardcoded in the theme |
| Identifiers | GTIN / MPN / brand on every variant | SKU only, agents cannot match products |
| Availability | Accurate at request time | Cached, agents quote stale stock |
| Feeds | Monitored and reconciled to Shopify | Set up once, silently drifting |
| Agent access | AI crawlers explicitly allowed and mapped | Blocked or undeclared in robots.txt |
| Machine-facing content | llms.txt plus fact-backed answers | Marketing prose a model cannot verify |
| Checkout | Prepared for agent-facing purchase | Human-only cart flow |
| Attribution | Agent traffic separated and reported | Lumped into direct or organic |
| Citations | Monitored across major assistants | Unknown — nobody is looking |
| Maintenance | Schema and feeds validated on every deploy | Breaks quietly on the next theme change |
| Dev capacity | Shopify engineers who ship the fixes | A report with nobody to implement it |
| Best for | Brands that want to be bought, not just found | Stores relying on brand search alone |
Get a free 30-min agentic readiness review — we will show you how AI surfaces currently read your catalogue.
Agentic commerce is a data problem before it is a marketing one — six steps from audit to monitoring.
Schema coverage, identifier hygiene, feed health, agent access and how assistants currently describe your brand.
Product, Offer and Organization schema generated from live Shopify data, with identifiers completed across the catalogue.
Merchant Center and agent-facing feeds wired to Shopify inventory, with monitoring so drift is caught early.
Availability, policy and identifier data prepared for agent-facing checkout and Universal Commerce Protocol rollout.
llms.txt, question-led structure and verifiable claims so assistants cite you accurately instead of paraphrasing rivals.
Agent traffic separated in analytics, citation tracking, and a monthly view of what AI surfaces actually send you.
Three transparent engagement models. Fixed scope, fixed price — you know the number before we start.
One-time audit of schema, feeds and agent access.
2–3 weeks
Book an auditFull agentic readiness build for one Shopify store.
5–8 weeks
Start implementationOngoing agentic readiness as the standards move.
Rolling, cancel with 30 days
Talk to an engineerNeed a fixed-scope proposal? Tell us about your catalogue and we will scope it in writing.
Agents filter on different attributes in every vertical — the data model changes accordingly.
Size, fit, material and colour as structured attributes an agent can filter, plus variant-level availability that stays honest.
Ingredient and shade data modelled properly, with claims an assistant can verify rather than repeat.
GTIN and MPN completeness, spec tables as data, and compatibility attributes agents use to shortlist.
Allergen, origin and dietary attributes structured so agents can answer restriction questions correctly.
Dosage and compliance-safe claims, with regulated wording an assistant will not overstate on your behalf.
Tiered pricing, MOQ and lead-time data exposed where agent-assisted procurement can read it.
Shopify-native engineering, schema generated from live data instead of hardcoded, feeds that stay reconciled, and attribution that proves what AI surfaces send. We build the layer agents actually read.
Get a readiness auditOn-site AI — search, recommendations, assistants — the counterpart to being readable off-site.
Classic search and AI surfaces share one foundation: structure, depth and verifiable claims.
Stack audit that finds the apps and scripts breaking your structured data on every deploy.
Themes that emit schema from real product data rather than hardcoding it into templates.
Tiered pricing and MOQ data structured for agent-assisted procurement.
Retained capacity to keep schema, feeds and protocols current as standards shift.
Agentic commerce is when an AI agent — ChatGPT, Perplexity, a shopping assistant — browses, compares and increasingly buys on a shopper's behalf. The practical consequence for a Shopify store is that your product data, not your storefront design, decides whether you appear in the answer.
Agentic commerce overlaps with SEO but is not the same. SEO optimises for ranked links a human clicks. Agentic readiness optimises for a machine that never renders your page — it reads schema, feeds and APIs, then acts. Same foundation, different consumer.
No. Schema, feeds, identifiers and llms.txt all work on standard Shopify plans. Shopify Plus adds checkout-level control that matters once agent-facing checkout matures, but the data foundation is identical.
The Universal Commerce Protocol is an emerging standard for letting agents complete purchases across merchants without a human in the cart. It is still moving, which is exactly why we focus first on the parts that are stable and already pay off — identifiers, availability accuracy and policy data.
Yes, partially. Assistant referrals can be separated in analytics, and citations can be tracked across major assistants month over month. It is less precise than paid reporting — we say so rather than invent a number.
For a store that wants to be bought, blocking them removes you from the shortlist. Our own robots.txt explicitly allows GPTBot, ClaudeBot and PerplexityBot. Publishers protecting paid content have the opposite calculus — merchants generally do not.
An audit takes 2–3 weeks. A full implementation runs 5–8 weeks depending on catalogue size and how much identifier data is missing. Schema and feed work usually shows up in assistant answers within a few weeks of shipping, not months.
Honestly: it is early, and anyone quoting you a precise multiple is guessing. What is measurable today is citation share in assistant answers and referral traffic from AI surfaces. The stronger argument is defensive — the data work also fixes classic search, feeds and merchandising, so it pays even if agent traffic stays small.
Yes. Custom Shopify apps, metafield structures, feed transformers and middleware where the native connectors do not expose what an agent needs. See Shopify app development.
The data involved is your public catalogue — products, prices, availability, policies. No customer data is exposed to agents. Where agent-facing checkout arrives, it runs through Shopify's own authorisation, not a side channel we build.
Shopify exposes store data to AI assistants over the Model Context Protocol (MCP). A storefront endpoint answers catalogue, cart and policy calls — an agent uses tools such as search_catalog, get_product, get_cart and search_shop_policies_and_faqs, with the UCP catalogue served from its own endpoint. The calls need no authentication, and access can be restricted per store. So the work is rarely about switching MCP on; it is about what the agent finds when it calls — accurate identifiers, real availability, and policies written so an assistant can quote them without inventing terms.