Adaptive Transformer Search for ecommerce

Adaptive Transformer Search is Particular Audience’s ecommerce search offering. Evaluate it through the queries your shoppers use, the products and attributes you supply, and the controls your team needs. Combine relevance assessment with practical integration checks and a live measurement plan before making a rollout decision.

Help shoppers move from a query to a useful product

Retail searches range from precise product references to descriptions of an occasion, problem or preferred feature. A useful search experience needs to interpret that request, retrieve suitable candidates and present a result the shopper can act on.

ATS provides the search foundation for this journey. The implementation overview documents vector search, suggestions and redirects, with behavioural events supporting learning and analytics. Treat those capabilities as components to configure and verify against your own assortment.

Keep requirements visible through the result journey

Create a test set containing exact identifiers, brands, descriptive phrases and combinations such as size and price. Specify which parts are mandatory. A semantically related item is not automatically a suitable substitute for an unavailable exact product.

Check the complete path from search response to product page: titles, prices, images, variant selection, selected filters and result counts should agree. Inspect edge cases where a required field is missing or an item becomes unavailable during the journey.

PA’s Search Query documentation covers request context, result data and optional semantic-model metadata. Use the agreed configuration and model version when recording a benchmark so changes can be reproduced.

Give the commercial team controlled influence

Decide which merchandising requirements are business constraints and which are preferences. Keep promotional aims visible, give exceptions owners and test their effects on representative queries. An inherited set of rules deserves review when retrieval or ranking changes.

Merchandiser Control provides the product context for influencing search, listings and recommendations. Ask to see the controls and precedence that apply to the configuration you are buying rather than assuming every requirement is already enabled.

Plan the data and measurement work

Agree catalogue ownership, update handling, event definitions and the initial storefront scope. Include loading, empty-result and error states in implementation acceptance. Check the relevance of exact and descriptive queries before considering a broader rollout.

Use an offline comparison to diagnose why results change. Use a separately designed live test to estimate effects on the chosen commercial outcome, with conversion, latency and availability guardrails. Neither an AI label nor a vendor-wide average predicts the effect for a particular retailer.

Bring your current query examples and integration requirements to a Search Evaluation discussion. Start with a decision that can be tested, and expand the implementation when the evidence supports it.

A buyer checklist for AI product search

Ask each shortlisted supplier to demonstrate the same difficult journeys: an exact product reference, a descriptive query, an unavailable item and a query with mandatory filters. Record the products returned and the configuration used. A polished demonstration on a different catalogue does not answer those questions for your business.

Agree the scope of the purchase. Does it cover retrieval, ranking, the storefront interface, analytics or ongoing tuning? Identify who owns data mapping, implementation, incident response and changes after launch. Capture separately priced modules and services in the commercial comparison.

Require evidence for each important requirement: current documentation, a configured demonstration, a measured test or a contract commitment. Mark unanswered items as unverified rather than assuming either support or absence.

Finish with a decision plan: which findings would justify a pilot, which would block it, and what the live test needs to measure. This turns an AI product-search discussion into a practical procurement exercise with a traceable next step.

Frequently asked questions

What does ATS stand for?

ATS means Adaptive Transformer Search. It is Particular Audience’s ecommerce search product, evaluated through the configured search experience rather than the name of the model architecture alone.

Will ATS remove all manual merchandising?

Do not plan on eliminating human ownership. Teams still define product data, business requirements, exceptions and evaluation criteria. Measure any workload change in your own operation.

Can we keep a precise product-code journey?

Include product codes and other exact references in the acceptance set and demonstrate their handling alongside descriptive queries. Require the requested item or a clearly identified no-match outcome; a related product is not an exact match.

What should an AI search demonstration use?

Use representative queries, product data and constraints from your own business. Ask the supplier to save the tested configuration so results can be inspected and repeated.

Should we compare feature counts?

Feature lists are useful for screening, but assess each requirement’s fit, evidence, operating cost and package. A feature name alone does not show that the required workflow works.

What should remain outside the first rollout?

Keep optional surfaces or modules outside the initial scope unless they are necessary for the decision. Name their future dependencies so the first implementation is measurable and manageable.