LLM Visibility II — Making the Catalogue Legible

Why this module exists

Module 3 established where you stand. This one is the work you can do on your own estate — Module 5 is the work you cannot.

It is also the module where a leader most needs enough technical grip to specify and challenge, without being able to implement. So it is written at that level: precise about what must be true, explicit about how to verify it, and silent about how to code it.

The scale of the opportunity is unusual. Adobe's 2026 analysis of US retail scored individual product pages at 66% on machine-readability — the worst-performing page type, below category pages at 74% and homepages at 75%. The pages that hold the facts an engine needs are the least readable. Most competitors are in the same position, which is why this work still returns disproportionately.

4.1 The four layers

Work the layers in that order. A perfect page nobody may fetch is worth nothing, but an accessible page with no facts in it is worth almost as little — and the feed reaches surfaces the page never will.

4.2 Layer one: the feed

For agentic surfaces the feed is not an export. It is the interface. Google's Universal Commerce Protocol, announced in January 2026, works from merchant catalogue data; agent surfaces increasingly answer from feeds rather than crawling pages. If a fact is not in the feed, it does not exist on those surfaces.

Three properties decide whether a feed is fit for machines.

Completeness beyond the required set. Platform-required fields get you listed. The optional ones get you answered. A shopper asking for a washable hallway rug under €200 in a dark colour is asking about four attributes; if you publish two, you cannot be the answer.

The attributes that carry disproportionate weight are the ones that appear in real questions: material and composition, dimensions, care and washability, colour family as well as marketing colour name, compatibility or fit, certifications, warranty period, country of origin, and energy or safety ratings where they apply.

Freshness, measured in minutes. Price and availability accuracy have become discovery inputs. An agent that recommends an out-of-stock product does not do it twice, and platforms increasingly demote merchants whose data does not match their site. Ask your team one question: what is the maximum age of the price an external surface can currently see? The answer is frequently "up to 24 hours," and is frequently a surprise to the commercial team.

Consistency with everywhere else. Your feed, your page, your marketplace listings and your distributors' listings should agree. Where they disagree, a synthesising model resolves the contradiction, and you do not control which way.

4.3 Layer two: structured data

Schema.org markup is how a page states, in a machine-parsable form, what it is describing.

The distinction that matters: presence is not validity, and validity is not coverage. Nearly every retailer has some Product markup. Far fewer have markup that validates without errors across the whole catalogue, and fewer still mark up the things that answer questions.

Prioritised for commerce:

Three rules that separate a real implementation from a checkbox:

Mark up what a customer asks, not what a validator accepts. Returns policy and delivery terms are among the most-asked questions in commerce and among the least-marked-up entities on the web. Module 3's prompt set almost always shows post-purchase prompts answered from a wrong third-party source; this is the fix.

Identifiers are not optional. GTIN, MPN and brand are how a machine knows your product is the same product it saw elsewhere. Without them you are a different item on every surface, and cannot win a comparison you are not recognised as being in.

Validate at catalogue scale, continuously. Spot-checking three pages proves nothing about 14,000. Validation belongs in your release process, with an error budget, like any other test.

4.4 Layer three: rendering

The question is simple and rarely asked: what does a machine receive when it fetches your page?

Many storefronts assemble the commercially important facts — price, availability, specifications, reviews — in the browser, after load, by script. A human sees them. A crawler that does not execute scripts, or gives up before they finish, does not.

Engines vary in what they execute and how patiently. Some render; some do not; some render on a delay measured in days. Designing for the least capable consumer is the safe position, because you cannot tell which one decided not to cite you.

The test, which anyone can run in two minutes: fetch a product page without executing JavaScript — view-source in a browser, or curl the URL — and search the returned text for the price, the availability, the top specification and one review. Whatever is missing is invisible to a meaningful share of machines.

Two further points that are routinely missed:

• Blocked subresources. If your robots policy blocks the paths serving your structured data or critical assets, a crawler that would have rendered cannot.
• Interstitials. Age gates and region redirects that a machine cannot pass turn every page behind them into an empty document. Consent walls are a special case and are dealt with below.

4.5 Layer four: access policy

Somebody has already decided which machines may read your catalogue. In most organisations it was decided by an edge provider's default, applied in a configuration nobody reviewed.

The landscape has hardened in the last two years. Cloudflare moved to blocking AI crawlers by default, introduced pay-per-crawl, and has continued tightening — from September 2026 its defaults block "mixed-use" crawlers on pages carrying ads. Cryptographic bot identification through Web Bot Auth is becoming the mechanism for telling a real agent from something wearing its user-agent string.

The decision is not binary, and framing it as "block or allow" is what produces bad policy. Four questions produce a defensible one:

1. Which machines create demand? The crawler behind a shopping surface your customers use is a distribution channel. Blocking it is a decision to be absent.
2. Which consume without returning anything? Bulk training crawlers and scrapers cost bandwidth and give nothing back. Different answer.
3. What does it cost? Aggressive crawling is a real infrastructure line, and a large share of it re-fetches pages that have not changed.
4. What must not be taken? Pricing intelligence and full catalogue extraction are competitive exposure, distinct from being discoverable.

A defensible default for most retailers: allow the crawlers behind shopping and answer surfaces your customers use, throttle or charge bulk training crawlers, require identification for agents that transact, and review the list quarterly — because the list changes faster than your infrastructure roadmap.

4.6 The audit, with pass criteria

Scored per layer. A criterion without a threshold is an opinion.

Two honest notes on the thresholds below. Where a platform publishes a requirement — identifiers, price-and-availability accuracy — the threshold follows it. Everywhere else these are the author's judgement about what is achievable and worth paying for, not a derived industry standard. Argue with them; what matters is that your version is written down before you measure, not after.

Freshness in particular has a cost. Moving from a nightly rebuild to sub-hour deltas is real engineering — an incremental pipeline, change-data capture on price and stock, and an availability service that can take the read load. Price that before you commit to the number.

Score honestly, publish the score, and re-run it quarterly against the Module 3 baseline. The pairing is the point: this table is the input, the prompt set is the outcome.

Exercise — Audit and specify

Time: 2–3 hours. Produces the artefact for this module.

1. Score the eight checks on your own estate. Where you cannot measure a check, that is a finding — write "unmeasured," and note who would need to measure it.
2. Run the buying-question test from 4.2 on your ten best sellers. Count how many questions are answerable from structured attributes.
3. Run the rendering test from 4.4 on three product pages. List what is missing.
4. Find out who owns your access policy today. If the honest answer is your CDN's default, write that sentence down; it is the most persuasive line in the document.
5. Write the remediation specification: each item with an owner, a pass criterion, an effort estimate and a sequence position. Order by effect per week of engineering, not by score.
6. Book the re-measurement against your Module 3 prompt set, with a date, before you start.

Self-check

1. Why is the feed, rather than the website, the interface that decides visibility on several surfaces?
2. Your structured data "is implemented." What two questions establish whether that is worth anything?
3. What does the no-JavaScript fetch test tell you that a site audit tool usually does not?
4. Give the four questions that turn an agent-access decision into a policy.
5. Which of the eight checks would you expect your organisation to fail today, and who owns the fix?

Further reading

• Adobe, AI traffic grows but retail sites lag in AI search visibility, 2026 — the machine-readability scoring.
• Google, Universal Commerce Protocol and Merchant Center product-data documentation, 2026.
• Schema.org: Product, Offer, MerchantReturnPolicy, ShippingDetails, FAQPage.
• Cloudflare, AI crawler control and Web Bot Auth posts, 2025–2026.