Particular Audience Particular Audience
Talk to us

The retail operating system

One engine for every retail decision.

Most retailers run four or five separate tools.

Search in one tool, pricing in another, ads bolted on top — nobody owns the order shoppers see, and margin leaks between them. The Unified Decision Engine governs relevance, inventory, margin and sponsored demand in one pass, on every surface, including the AI assistants now doing the shopping.

Start with one surface. Leave the rest of your stack untouched.

Need one thing first? Start with our AI product recommendation engine or AI site search.

AI Product Attribute Enrichment for Ecommerce Search

Improve product attributes for search with a controlled AI enrichment workflow. Define evidence, review uncertain values and map approved data into your catalogue.

How to enrich product attributes for ecommerce search

AI-assisted enrichment can propose structured attributes from existing product evidence, but proposed values need validation before they drive search, filters or recommendations. Define the schema and source hierarchy, review uncertain or consequential claims, and publish approved attributes through your catalogue pipeline with clear provenance and version control.

Start with a search requirement

List the questions your catalogue must answer: colour, material, pack size, product type, dimensions or compatibility. Separate descriptive preferences from facts that determine whether an item is suitable. Identify whether each field belongs to a product, a variant or a location-specific offer.

Use a controlled vocabulary and explicit units. Keep missing, unknown and not applicable distinct. A blank compatibility field should not silently become compatible, and a number without a unit should not become a valid dimension filter.

Generate candidates from identifiable evidence

Provide the enrichment process with approved source material such as supplier specifications, existing descriptions or product imagery. Ask it to return a proposed value and the supporting source location. Keep generated suggestions separate from authoritative values until reviewed.

For a fictional jacket, imagery might support a proposed colour, while a waterproof-performance claim needs an appropriate product specification. If the source does not establish the fact, retain unknown. A plausible answer is not enough to make an attribute trustworthy.

Review the fields that matter most

Validate field types, units, allowed values and product/variant scope automatically where possible. Route conflicting or unsupported values for human review. Audit samples by category and attribute, with particular attention to facts that affect suitability or customer expectations.

Store source, extraction version, reviewer decision and approval time in your own data workflow. These are proposed governance fields, not a claim that every search API accepts them unchanged.

Map approved data into search

PA’s Products API supports structured attributes. Its current documentation says omitted fields are cleared, so do not treat an enrichment-only payload as a safe partial update. Agree the integration contract and inspect acceptance errors before changing live catalogue records.

For Shopify, PA field configuration requires explicit mapping for custom metafields and a product sync after configuration changes. Importing a field and generating its value are separate operations; those documents verify ingestion, not a standalone PA AI-enrichment service.

Measure quality before expanding coverage

Check approved-value coverage alongside sampled accuracy and unresolved conflicts. Test whether the enriched fields improve relevant retrieval and useful facets without introducing false matches. Retain a baseline snapshot so changes can be traced or reversed.

Discuss your schema, evidence sources and required attributes with the search implementation team. A small, reviewed category pilot can reveal mapping and data-quality issues before they affect the rest of the catalogue.

Illustrative example

No numerical illustration needed; qualitative examples in the body are hypothetical unless explicitly attributed.

Sources

How Particular Audience does it

Frequently asked questions

Can AI fill every missing product field?

A model may propose a value, but missing evidence must remain visible. Do not approve an attribute simply because the output is plausible or syntactically valid.

Does PA field mapping generate the attributes?

The checked Products API and Shopify configuration documentation describe accepting and indexing product data. They do not establish a separate automatic attribute-generation service; confirm any requested enrichment service with the product team.

Can we send only the enriched fields to the Products API?

Do not assume a partial-update contract. The current Products API warns that omitted fields are set to null or empty. Confirm the endpoint and payload semantics with the implementation owner before sending changes.

Related guides

  • How to evaluate ecommerce site search: Evaluate ecommerce search with representative queries, calibrated relevance labels and clearly defined metrics. Validate commercial impact with a planned live test.
  • Site search for fashion ecommerce: Build fashion site search around style, size and availability. Learn where semantic retrieval, exact filters and visual alternatives help, and how to test them.

How-to guides · All guides