01 / Customer question
The commercial context
Product visibility is only useful when the offer is accurate.
AI shopping experiences need to identify the right product, match the customer's requirements and preserve the offer they can actually buy. Missing variants, stale prices and unclear availability create gaps between a recommendation and an order your business can fulfil.
For a large retailer, the challenge spans the product information management system, product pages, feeds, stock and fulfilment. We assess those connections across a defined assortment and market. The objective is accurate discovery and a viable route to purchase, with customer data and commercial intelligence kept under appropriate control.
Customer journeys
Follow the shopping journey through to fulfilment.
We agree the questions, audiences and markets to assess. The examples below show what we can investigate, with the final scope based on your priorities.
02 / Customer question
Can the shopper buy the correct variant with delivery or collection in their location?
03 / Customer question
Does the recommended offer preserve the actual price, returns terms and warranty?
01
Catalogue and discovery assessment
We reconcile product identities, variants, images and specifications across your catalogue, product pages and feeds. Journey evaluation checks whether AI finds and matches the actual products you sell.
You receive: Coverage, matching and product-information issue register.
02
Platform and transaction planning
We assess relevant Merchant Center and UCP options alongside the separate requirements for ChatGPT product discovery and checkout. The plan identifies product, market and platform eligibility before proposing integration work.
You receive: Eligibility matrix, integration options and scoped commerce pilot.
03
Commercial evaluation
We define measures with your analytics and commerce teams, covering orders, margin, cancellations and returns. A pilot establishes what can be attributed and what needs a suitable comparison to assess incremental value.
You receive: Measurement design and criteria for profitable continuation.
Internal AI readiness
Align buying, commerce and service teams.
Merchandising, buying and customer-service teams need reliable product knowledge and a clear owner for updates. We assess where an assistant can help colleagues retrieve specifications, understand fulfilment conditions or handle routine documents.
Permissions for purchases, refunds and account changes are separate decisions. The roadmap covers PIM, ERP and order-management dependencies, staff training and task-quality evaluation. A useful pilot should demonstrate appropriate actions and commercial value before wider rollout.
A practical first engagement
Retail AI Commerce Readiness Assessment
We scope categories, representative SKUs and variants, markets, channels and fulfilment routes. The assessment reviews existing product feeds and shopping journeys, then delivers a catalogue baseline, discovery findings, an eligibility and integration matrix, a prioritised backlog and a pilot business case.
01
Agree the scope
We agree the business questions, reference sources, AI platforms and teams involved. Before the assessment begins, we confirm the access needed and any work it depends on.
02
Review the evidence together
We separate what the AI answer shows from the content, data or workflow issues behind it. Your teams help decide which changes matter and which are feasible.
03
Choose the next action
The plan identifies the priorities, who is responsible, what each change depends on and how it will be checked. If a pilot is worthwhile, we agree how to run and evaluate it with the teams involved.
Evaluation
Measure the changes that matter.
We agree how to evaluate results before making changes. Each answer is recorded with its question, market, AI platform and date. Repeat checks help identify patterns. We assess commercial results and improvements to internal workflows separately with your teams.
Catalogue coverage
Product matching
Offer freshness
Order margin and returns
Part of Ayima
Technical search expertise. Practical enterprise thinking.
SWLR combines AI visibility and readiness consulting with Ayima's experience in enterprise search. We investigate the content, data and technical issues behind your business question, including access permissions, then help your teams decide what to do next.
Common questions
Before we begin.
Does UCP also enable sales in ChatGPT?
No. UCP provides a commerce standard with a Google implementation. ChatGPT discovery uses its own product-feed requirements and checkout needs a separately enabled integration. We assess each route against your products, markets and platform.
Do we need to replace our ecommerce platform?
The first step is to assess your existing catalogue, commerce and fulfilment systems. Integration paths depend on that stack and platform eligibility. A replacement should only be considered where the evidence supports it.
How will we know whether AI generates profitable orders?
We agree how to track eligible journeys, referrals and orders, including margin, cancellations and returns. A comparison or pilot design is needed to distinguish new value from demand that would have reached you through another channel.
Fashion, Apparel & LuxuryAssess how AI presents your collections, styling and product information, then plan changes that support accurate discovery and your brand standards.
FMCG / Consumer Packaged GoodsAssess how AI recommends your products for real needs and occasions, then improve the data connecting brand discovery with retail availability.
Home Improvement, Garden & Trade SuppliesAssess how AI recommends materials, tools and quantities, then improve the information behind project-led shopping and trade support.
Signal → Evidence → Action
Start with the questions that matter.
Discuss your assortment, markets and the shopping journeys you want to improve.
Discuss your AI strategy