The next customer at your checkout is an agent.

It overlaps, and we will not pretend otherwise: clean structured data and crawlable content help both. The difference is the target. Traditional SEO optimizes to win a click from a results page. Agentic commerce optimizes so a machine can extract, quote, and recommend your product accurately inside an answer, often without a click at all. That shifts emphasis from keywords and links toward data completeness, factual accuracy, and machine-readable formats.

No, and nobody can. These systems do not expose ranking controls, their behaviour changes without notice, and recommendations depend on the phrasing of a prompt we do not control. What we can do is remove the technical reasons you are excluded or misquoted: missing schema, wrong prices, incomplete attributes, unreadable specifications. Think of it as being eligible and accurate rather than guaranteed placement.

Three ways, and none of them is a ranking report. First, a documented baseline of how assistants currently answer your key buying questions, re-run over time. Second, referral traffic from assistant surfaces, which is increasingly identifiable in analytics. Third, technical completeness metrics such as schema validity and feed attribute coverage, which we can measure directly. The first is qualitative by nature, so we record it as evidence rather than dressing it up as a metric.

It depends on your category and margin. The honest position is that assistant-driven traffic is still small for most brands but growing, and nearly all the underlying work is worth doing regardless: accurate schema, complete feeds, and extractable specifications also improve conventional search, marketplace performance, and merchant feed approval rates. If your catalog data is already excellent, waiting is reasonable. If it is not, this is the same cleanup you needed anyway.

Usually four things in the same order. Specifications trapped in images, so no crawler can extract an attribute. Missing or invalid Product and Offer schema, so prices and availability are guesses. Incomplete identifiers such as GTIN or MPN, which break matching against known products. And stale cached content quoting prices you no longer charge. The audit ranks these by how much each is costing you.

Not to start. For most catalogs under a few thousand SKUs, Shopify metafields plus disciplined data entry are sufficient, and adding a PIM is premature cost. A PIM starts earning its keep when you sell across several channels with different attribute requirements, manage multi-language product content, or have product data originating in an ERP that needs enrichment before publishing. We will tell you which side of that line you are on rather than selling a system you do not need.