Direct answer: start with one category, the attributes that change a buying decision, and sources that can substantiate them. Fund extraction, normalization, review, and publication—not a batch of generated descriptions. Separate one-time preparation and integration from recurring change processing and exception handling. Scale only when correct facts reach the filters, product pages, and channels that consume them.
When onsite search fails to surface available products, leadership faces an investment choice: repair a few records manually, configure existing tools, or build a repeatable enrichment operation. This project sits upstream of ranking and recommendations. Its job is to convert specifications and supplier documents into usable attributes while preserving uncertainty. Persuasive copy cannot repair a compatibility fact attached to the wrong variant.
Scope the buying decision, not the entire catalog
Choose a category with observable demand and a specific friction point: shoppers cannot filter dimensions, compare materials, or identify compatible models. Shopify documents category-linked attributes and metafield definitions with shared validation rules. These illustrate separate decisions: what a category needs to describe and which values a field may accept. Establish both before deciding whether additional software is necessary.
- Build a scope sheet with one row per relevant attribute and variant.
- Buying decision: which comparison, filter, or question does the field support?
- Authorized source: manufacturer documentation, a validated master record, or another identifiable source.
- Definition: meaning, unit, allowed values, market, and inheritance between product and variant.
- Destination: product page, search index, feed, or recommender that will consume it.
- Acceptance: required evidence, owner, test, and behavior when the fact is unavailable.
Exclude fields without a real consumer. A populated PIM field that never reaches a filter or page may improve no shopping decision. Distinguish product dimensions from shipping dimensions, capacity from power, and a marketing color name from material. Keep unknown, not applicable, and conflicting as separate states. A catalog that is technically complete but factually speculative is not ready for automation.
Separate four different jobs
- Extraction finds an explicitly stated fact and attaches it to the correct model and variant.
- Normalization converts units, formats, or vocabulary through reproducible rules while retaining the original.
- Classification proposes a category or approved option, with ambiguous cases routed for review.
- Writing turns accepted facts into useful copy without adding unsupported features or promises.
AI can assist document reading and classification. Unit conversion and type validation usually belong in deterministic code. Akeneo documents configurations that select context attributes and assets and preview output before automation; that capability does not verify the truth of each generated value. Test tools already licensed. A dedicated pipeline becomes useful when source diversity, decision rules, or destination requirements exceed what existing configuration can handle with traceability.
A U.S. example: charger compatibility, not better adjectives
Consider a family of USB-C chargers with different power profiles and included cables. A family brochure is not proof that every SKU supports every profile. The workflow must link the specification to the exact sellable variant, distinguish a charger’s maximum output from a cable’s supported rating, and preserve the manufacturer’s compatibility wording. It must not infer certification from an image or apply one model’s capability across the family. This is a hypothetical example, not a client case.
Keep source identity, version or date, supporting passage or page, original value, normalized value, and approval with each proposal. If manufacturer and distributor records conflict, apply a pre-agreed source priority or send the case to the catalog owner. Preserve original metric values when producing a U.S. display conversion. Price, inventory, and delivery promises should still come from their authoritative commercial systems, not this enrichment process.
Budget accepted work, not generated output
A useful effort model is fixed preparation plus integration and tests plus source processing plus human review plus correction and ongoing operations. Cost per thousand SKUs conceals major differences in variants, documents, and fields. Track cost per accepted, published attribute and cost per product ready for the chosen buying decision. Keep shared family facts separate from variant-specific facts so the estimate does not count the same decision repeatedly.
Illustrative arithmetic only: 2,000 variants with eight candidate fields create up to 16,000 field checks before shared-field deduplication. If a pilot measures a 25% exception rate and three minutes per exception, review alone takes 200 hours: 16,000 × 25% × 3 ÷ 60. These are assumptions, not a forecast or benchmark. Replace them with measured sample results and add quality assurance for automatically accepted records.
Price those hours using the relevant team’s loaded dollar cost, then add licensing, model processing, OCR where needed, integration, monitoring, and maintenance. Compare against manual correction and native features already paid for. Released capacity is not automatically a cash saving. Conversion improvement is a separate hypothesis, not another name for the same labor benefit. Low token costs do not rescue an expensive, ownerless review queue.
A six-week implementation scope
- Week 1: select the category and decision; audit sources, variants, definitions, and current errors.
- Week 2: create a representative evaluation set with expert-reviewed answers, including poor documents, conflicts, and missing facts.
- Week 3: run extraction and rules in shadow mode without writing to the public catalog.
- Week 4: measure field-level errors, tune review routing, and test publishing and rollback safely.
- Week 5: release an approved batch and verify the page, filter, index, and destination feed; record rejected values.
- Week 6: review accepted-data cost, review capacity, and commercial evidence; expand, narrow, or fix the source.
Sample size and the commercial observation window depend on volume and consequences; six weeks does not guarantee a conclusive conversion test. Catalog and merchandising own meanings and sources. Engineering owns mappings, import behavior, and rollback. Marketing owns the buying decision and outcome measurement. Specialists review sensitive claims. Implementation support can build the system, but someone inside the company must resolve conflicts and maintain it after launch.
A valid field can still be a false fact
Measure required-field coverage separately from factual accuracy in an independent sample, variant assignment errors, unit consistency, traceability, and successful propagation. Set acceptance thresholds by consequence before reviewing results. Compatibility, safety, and regulated claims need specialist validation; model confidence alone is not evidence. Retain the previous approved value and a batch-level change log so a correction can be reversed without reconstructing the whole catalog.
Google documents product_detail as structured technical information. A correctly formatted submission does not guarantee visibility or sales. Map each destination deliberately instead of copying one text blob everywhere. Where traffic allows, evaluate improved data against the prior experience using a valid comparison, measuring search completion, conversion, and contribution alongside support questions and returns. If pricing, acquisition mix, or stock change at the same time, state the attribution limit.
Start with the search diagnosis at https://makinai.co/insights/en/ai-onsite-search-missing-products-ecommerce-conversion. Better data also supports recommendations at https://makinai.co/insights/en/ai-product-recommendations-ecommerce-conversion-margin, while inventory eligibility remains a distinct operating flow at https://makinai.co/insights/en/inventory-aware-ai-marketing-paid-media-crm.
Bring one category, a sample of authorized specifications, and the buying decision that fails today to a conversation with MAKINAI at https://makinai.co/services/en/crm-ecommerce-commerce-transformation. We can help define fields, destinations, review workload, and investment before broadening the technology. The scope sheet, arithmetic, and pilot are editorial recommendations—not vendor quotations, client outcomes, or conversion guarantees.