How to prepare your catalog for AI-powered search and agentic checkout

A human visitor forgives a lot. They infer that "3/4 in." and "0.75"" are the same dimension, they open the PDF to find the amperage rating, they tolerate a hero image with the spec table baked into the JPEG. A model does none of this reliably. It extracts what is explicit and structured, and it treats everything else as missing.

That single fact reorganizes your priorities. To recommend or buy a product, an AI system needs to answer three questions from your data alone:

• What is it, precisely? Typed attributes, specs, materials, dimensions, units — not adjectives.
• What is it for and what does it fit? Use cases, applications, compatibility and fitment, the buyer's situation in plain language.
• Can it be transacted right now? A stable identifier, a real price, in-stock availability, and the shipping and return terms needed to compute landed cost.

Miss the first two and you are invisible to discovery. Miss the third and you are visible but un-buyable — the agent surfaces a competitor it can actually complete a cart with. The rest of this guide is organized around closing those three gaps at catalog scale, because one beautifully enriched SKU does nothing; the model judges your whole catalog's legibility.

Treat "AI search" and "agentic checkout" as two readiness layers with different owners and different acceptance tests. Conflating them is why projects stall.

Discovery layer (be found and cited). This is the content and structure that lets an answer engine, an on-site semantic search, or a marketplace AI understand and recommend your product. The currency is completeness and clarity: attributes, descriptions, use context, structured markup. The test: given only your catalog data, can a model correctly tell a buyer what this is, who it's for, and why it fits their stated need?

Transaction layer (be bought by an agent). This is the operational data and plumbing that lets an agent put your item in a cart and pay. The currency is accuracy and freshness: a stable product ID, real-time price and inventory, shipping options, tax, and return policy, exposed through a feed or protocol (ACP product/checkout specs, an MCP commerce server, or your marketplace's agentic API). The test: can an agent compute total landed cost and complete a purchase that won't bounce back as a return or a cancellation?

You can win the discovery layer and still lose the sale if the transaction layer is stale. You can have perfect inventory sync and never get surfaced because your content is thin. Most teams are stronger on transaction (they already run feeds to Google Merchant Center and marketplaces) and weaker on discovery. Audit both before you invest.

Identity is the foundation both layers stand on, and it is the most common silent failure. Agents and answer engines resolve and compare products by stable identifiers. Without them, a model cannot confidently tell that your listing and the manufacturer's spec are the same item, cannot match you in a comparison, and on several agentic surfaces simply won't include the SKU.

Work through this in order:

1. Assign a GTIN/UPC to every sellable unit. Google has signaled for years that unique product identifiers improve eligibility and matching; agentic surfaces inherit that logic. Items genuinely without a GTIN (custom, bundled, private-label) need a clear, consistent fallback — a real MPN plus brand.
2. Normalize brand to one canonical value. "3M", "3M Company", and "MMM" are three brands to a machine. Map them to one.
3. Carry MPN, and keep it clean. Manufacturer part numbers are how B2B buyers and their agents cross-reference. Strip the noise (trailing pack codes, internal SKUs jammed into the MPN field).
4. Model variants as variants, not as twenty orphan SKUs. Size, color, and pack-size should roll up to a parent with each child carrying its own GTIN. Flattened variant catalogs confuse comparison and create duplicate, competing pages.
5. Deduplicate. Two listings for the same physical item split your signal and let a model cite the worse one.

This is unglamorous and it is the highest-leverage thing on the list. A complete, deduped identifier layer is what makes everything downstream matchable.

Free-text descriptions are for humans; fields are for machines. The single biggest discovery improvement for most catalogs is moving specs out of prose and into typed, normalized attributes with consistent units.

What "good" looks like:

• Typed values, not strings. Voltage is a number with a unit, not "runs on 120v or so." Color is a controlled value, not free text.
• Consistent units, one system, with the unit named. Pick a convention (imperial or metric, or carry both explicitly) and apply it catalog-wide. "0.75 in" beats "3/4"" beats "three-quarter inch" only because it's parseable and consistent.
• A schema per category. A circuit breaker and a safety glove need different attribute sets. Map every SKU to its category's required and optional attributes, and measure coverage — what percentage of required fields are actually populated. Coverage, not row count, is your readiness metric.
• Compatibility and fitment as structured relationships. "Fits Model X, Y, Z" should be data an agent can filter on, not a sentence. For distributors this is often the deciding signal in a comparison.

The honest tradeoff: building category schemas and back-filling attributes across a large catalog is real work, and it's exactly the work that doesn't fit in a PIM's UI one SKU at a time. A PIM stores these fields; it doesn't populate them. This is the layer where an enrichment system (Anglera's core job) earns its keep — gathering missing specs from source documents and the manufacturer, normalizing units, and scoring each SKU's completeness against how buyers actually search — then writing it back to your PIM as the source of truth.

Answer engines don't reward clever copy; they reward content that maps to the buyer's question. The reframe: stop writing descriptions about the product and start writing the answers to what buyers ask before they choose.

Practical moves:

• Lead with the job, not the adjectives. "Cut-resistant glove rated ANSI A4 for handling sheet metal" tells a model the use case and the spec. "Premium, durable, best-in-class protection" tells it nothing extractable.
• Make application context explicit. Who is this for, in what setting, solving what problem, and what does it replace or pair with. This is the language a buyer types into an answer engine, so it's the language that gets you matched.
• Answer the real pre-purchase questions on-page. Compatibility, sizing, certifications, what's in the box, common substitutions. These double as FAQ content a model can cite.
• Cut the fluff that dilutes signal. "In today's fast-paced environment" and "seamlessly unlock" are pure noise to an extraction model and bury the facts that matter.
• Stay factual and consistent. Manufacturer-supplied copy duplicated across a hundred resellers gives a model no reason to cite you; differentiated, accurate, structured content does.

Great data the model can't access doesn't count. You need to publish it in the formats answer engines and agents actually read.

• Schema.org Product/Offer markup on every product page. Include gtin, brand, mpn, offers (price, priceCurrency, availability), aggregateRating/review where real, plus shipping and return details in the Offer. This is the lingua franca for both classic rich results and AI extraction.
• A clean product feed. Keep your Google Merchant Center / Shopping feed complete and current: title, description, GTIN, price, availability, and the category-specific attributes. Many agentic surfaces are built on or modeled after this feed structure.
• Adopt the emerging agentic specs deliberately. The Agentic Commerce Protocol (OpenAI + Stripe) defines a product feed spec, a checkout spec, and a delegated-payment spec; Google's AP2 standardizes agent-initiated payments; the Model Context Protocol (MCP) is becoming a common way to expose catalog and cart operations to agents. You don't have to implement all of them, but pick the surfaces your buyers use and meet their spec exactly.
• Don't trap data in PDFs and images. A spec sheet PDF or a JPEG with the dimensions baked in is invisible to most extraction. If a fact matters, it must exist as text and, ideally, as a field. Give images structured, descriptive alt text and multiple angles.

Rule of thumb: every fact a buyer or agent needs should be reachable in at least one machine-readable place — markup, feed, or API — not only in human-rendered HTML.

This is where discovery readiness converts into actual agentic sales — or fails. Agents transact on current data and abandon or mis-buy on stale data.

• Real-time, accurate price. Including the price an agent will actually be charged: any contract pricing, quantity breaks, and currency. A mismatch between feed price and checkout price is a top reason agents abandon a cart.
• True inventory and availability. "In stock" must mean in stock. An agent that completes a purchase against phantom inventory generates a cancellation, and agentic platforms penalize sellers whose carts fail.
• Structured shipping and lead time. Agents compute landed cost and delivery date, not list price. Expose options, costs, and realistic lead times. For B2B, freight and bulk handling need to be representable, not hidden behind "call us."
• Machine-readable returns and warranty policy. Return window, restocking fees, and conditions are inputs an agent weighs when choosing between you and a competitor. Vague policy reads as risk.
• Tax and total-cost clarity. The agent needs to land on a final number it can present and pay.
• Feed freshness and sync cadence. Decide your update SLA (near-real-time for price/inventory, daily or better for attributes) and monitor it. A perfect catalog that syncs every 48 hours behaves like a stale one during the hours that matter.

Don't boil the ocean. Sequence by impact and prove it on a slice before scaling.

A workable order of operations:

1. Audit (week 1). Score the catalog: GTIN coverage, attribute completeness by category, percentage of SKUs with structured markup, feed freshness. You can't manage what you haven't measured.
2. Identity (weeks 1–2). Fix GTIN/MPN/brand and dedupe. Highest leverage, unblocks everything.
3. Enrich a priority category (weeks 2–4). Take your best-selling or most-competitive category, fill attributes to high coverage, rewrite descriptions to buyer questions, add markup. Prove lift before scaling.
4. Wire the feeds and a transaction surface (weeks 3–5). Markup live, Merchant Center clean, and one agentic surface (an ACP feed, an MCP server, or a marketplace API) working end to end.
5. Scale across the catalog and set the freshness SLA.

Pitfalls that quietly filter you out:

• Treating it as a one-time project instead of an ongoing data discipline — coverage decays as SKUs are added.
• Enriching one hero SKU while the catalog stays thin; the model judges breadth.
• Specs locked in PDFs and image-baked spec tables.
• Missing or duplicate GTINs, and variants flattened into orphan SKUs.
• Inconsistent units across categories.
• Stale price/inventory in the feed — the fastest way to turn an agentic sale into a return.
• Doing the content work at the feed instead of upstream in the PIM, so every channel re-derives the same fixes. Enrich once at the source of truth and let every surface inherit it.