01 / Customer question
The commercial context
Brand demand must connect to the correct product on the shelf.
AI may influence which brands a customer considers for a meal, household task or personal-care need. The manufacturer needs the right product, formulation and pack to be represented, with a route to a retailer that actually carries it.
That differs from the retailer's control of assortment, inventory and checkout. Brand teams may influence approved content and product syndication while the retailer owns availability and the purchase relationship. We assess category consideration and data consistency within those practical channel boundaries.
Customer journeys
Test category, occasion and substitution questions.
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
Does a comparison preserve the actual formulation, pack size, ingredients and allergens?
03 / Customer question
Can discovery connect the correct SKU to a retailer carrying it in the relevant market?
How SWLR can help
Improve the product information behind category consideration.
01
Category and brand benchmarking
We evaluate brand and SKU presence by occasion, need, market and agreed competitors. The findings identify category-discovery gaps, own-label substitution patterns and material product claims.
You receive: Category-consideration baseline and source/claim gaps.
02
Product and retailer syndication
We reconcile GTINs, pack formats, formulations, brand references and retailer listings. The priorities address inconsistent ingredients, allergens or market applicability and the process for syndicating correct data.
You receive: Digital-shelf consistency audit and syndication priorities.
03
Channel and internal pilots
We assess retailer and DTC routes alongside permissioned brand, content or insight workflows. The plan identifies channel owners, approved-information processes and measures that the available data can support.
You receive: Channel strategy options, approved-content process and pilot measures.
Internal AI readiness
Connect brand, category and product-data ownership.
Formulation changes, claims approvals and retailer updates need coordinated ownership. We assess how brand, category and product-data teams retrieve approved information and maintain consistency across markets and channels.
Public product attributes are separate from confidential formulations, shopper records and commercial agreements. Internal pilots use appropriate permissions and review. Sales evaluation also needs realistic boundaries: a manufacturer should not assume direct access to every retailer's checkout or customer data.
A practical first engagement
FMCG AI Category & Digital Shelf Assessment
We agree brands, categories, occasions, markets, representative SKUs and retailer channels. You receive category findings, product and listing inconsistencies, content and access priorities, and a roadmap with channel owners and suitable approved-content or discovery pilot options.
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.
Category and occasion consideration
Correct SKU and formulation
Retailer data consistency
Distribution and measurable sell-through
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.
How is this different from retail AI consulting?
Retail work centres on assortment, inventory, fulfilment and checkout. Manufacturer work centres on brand/category consideration, product information and retailer syndication. The channel strategy follows who owns the systems and customer journey.
Can AI recommend the right regional formulation?
We assess explicit product identity, formulation and market references, then test relevant scenarios. Correct GTIN, pack and regional attributes help distinguish products. Evaluation is needed to identify remaining confusion or outdated retailer information.
How do we measure sales when retailers own checkout?
We use the retailer and channel data available within the agreed scope. Where order or sell-through evidence is unavailable, reporting separates observed discovery from sales outcomes. Attribution or incrementality cannot be inferred from a brand mention.
Retail & Omnichannel CommerceGive AI shopping experiences accurate product information and assess the route from recommendations to orders across your catalogue and markets.Consumer Health, Wellness & NutritionAssess how AI describes your ingredients, benefits and intended use, then improve approved information across your products, markets and channels.
Signal → Evidence → Action
Start with the questions that matter.
Discuss your brands, categories, markets and the retailers carrying your products.