01 / Customer question
The commercial context
A project recommendation needs more than a product description.
A project recommendation needs the correct materials, quantities and compatible components. Product descriptions alone may not explain a substrate, coverage rate or installation condition. Incomplete lists and wrong units can lead to unsuitable purchases and a project that cannot be completed.
Domestic DIY, seasonal gardening and professional trade customers ask different questions. We assess the selected tasks against technical references and local fulfilment. The scope includes where an assistant should stop and refer the customer to a product specialist or qualified professional.
Customer journeys
Test projects, quantities and suitability.
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
Are the selected tools/components compatible with the project and substrate?
03 / Customer question
Can a trade customer obtain the right stock, delivery and technical guidance locally?
How SWLR can help
Improve the data behind complete project baskets.
01
Project and assortment evaluation
We test representative projects against product specifications, units, coverage and local stock. The baseline identifies incomplete materials lists, inappropriate choices and the assumptions behind quantity calculations.
You receive: Project-completeness and suitability findings.
02
Compatibility and technical content
We review supplier documents, dimensions, component relationships and applicable installation or safety information. The priorities connect structured product data with the technical knowledge required to make a suitable choice.
You receive: Compatibility/quantity data model and content priorities.
03
Shopping and trade-support pilots
We assess eligible shopping routes and permissioned knowledge for store and trade-counter colleagues. A scoped pilot covers project completeness, trade-account requirements and appropriate technical or safety escalation.
You receive: Project-to-basket feasibility and a reviewed support-pilot brief.
Internal AI readiness
Connect supplier expertise with store and trade-counter workflows.
Supplier updates and technical approvals need clear owners across category, product-data and service teams. We assess knowledge retrieval, quotation assistance and the trade-account permissions relevant to the proposed workflow.
Some projects require specialist or professional advice. The plan defines those boundaries and the hand-off process, with geography and product applicability made explicit. Pilot evaluation looks at compatibility and quantities as well as basket completeness and colleague task quality.
A practical first engagement
Project-to-Basket AI Readiness Assessment
We agree project types, DIY or trade audiences, categories, locations, supplier references and fulfilment paths. You receive project findings, a compatibility and quantity-data backlog, content priorities, escalation requirements and options for a project-to-basket or colleague-support pilot.
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.
Basket completeness
Quantity and unit accuracy
Product compatibility
Returns and safe escalation
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.
Can AI assemble reliable project shopping lists?
Reliable lists depend on project assumptions, units, specifications and compatible products. We assess those foundations and test defined scenarios. A pilot should expose uncertainties rather than imply a generic list will suit every installation.
How do you handle advice for hazardous work?
The scope defines situations requiring a qualified professional, product specialist or emergency service. Approved references and specialist review inform those boundaries. AI assistance is not a substitute for required qualifications or safe working practices.
Will this also support trade customers?
Yes, with a separate view of trade-account permissions, quotation work, technical references and fulfilment. Professional customers may need different products, quantities and support from domestic DIY customers, even within the same catalogue.
Signal → Evidence → Action
Start with the questions that matter.
Discuss the project types, product categories and trade or consumer journeys you want to assess.