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 build the assessment around specific questions, audiences and markets. These examples show the kinds of journeys we can examine; the final scope follows 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?
How SWLR can help
Connect catalogue quality with commerce readiness.
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 enablement 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 define the business questions, references, AI surfaces and teams involved. Access requirements and dependencies are agreed before the assessment begins.
02
Review the evidence together
Findings distinguish observed answer behaviour from the underlying content, data or workflow issue. Your teams help establish which changes are material and feasible.
03
Choose the next action
The roadmap sets out priorities, owners, dependencies and validation. Where a pilot makes sense, we scope the implementation and evaluation work with the relevant teams.
Evaluation
Measure the changes that matter.
We agree the criteria before making changes. Answer samples are recorded with their questions, market, surface and date. Repeat checks help show patterns within that scope; commercial outcomes and internal workflow gains need their own evaluation with your teams.
Catalogue coverage
Product matching
Offer freshness
Order margin and returns
Part of Ayima
Technical search expertise. Practical enterprise thinking.
SWLR brings an AI visibility and readiness remit to Ayima's enterprise search background. We connect the business question to the content, data, access and technical work behind it, then help teams prioritise the next steps.
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.