1. [Home]() 2. [Use cases]() 3. AI shopping readiness [Growth & acquisition]() # Get products ready for AI shopping discovery. Products are difficult for shopping agents to find or understand. Trigger A target AI shopping channel or priority category is selected. A category readiness register Illustrative example Product Readiness gap Next action Trail jacket Material composition unverified Catalog obtains approved specification City tote Variant availability differs from feed Engineering reconciles inventory source Travel pouch Required fields accepted Growth checks observable discovery **What you get**Channel-readiness assessment, corrected product feeds and an update process. [Discuss this use case]() [How it works]() Led by operators with 15+ years building commerce products, teams and systems. [About us]() ## Today Product teams have listings and feeds, but cannot show which facts and channel requirements are preventing useful shopping discovery. ## With this in place A selected product category has verified source facts, validated channel records and an owner for updates and unresolved requirements. ## Who does what, step by step. ### Your team Your team chooses the surfaces to pursue and approves new product facts before they are published. ### AI AI compares approved product information with each surface’s requirements and real buying questions to find missing facts. ### Automation Feed mappings and validation rules prepare records for each surface and check what each one accepted. 01 02 03 04 1. 01 Step 1: Your team ### Choose the surface Agree the channel, market and product category, then check its current access and eligibility requirements. 2. 02 Step 2: AI ### Find missing facts Compare approved product information with the selected surface and actual buying questions. 3. 03 Step 3: Automation ### Implement readiness Map and validate relevant feeds, page information and update paths with the existing catalog and commerce systems. 4. 04 Step 4: Automation ### Check observable behavior Verify accepted records and accessible pages, then monitor discovery observations and referred customer activity. ### Guardrails - Use authoritative product facts; route missing information to its source owner. - Check requirements for the actual target channel; access varies by channel and market. - Technical eligibility and a successful feed do not guarantee recommendations or sales. ## What a first project includes. A bounded first build around your systems and team. We agree the scope, fee and baseline before we start. [Scope this with us]() [How we work together]() Sample scope AI shopping readiness ### We build - A channel-specific readiness assessment and prioritized implementation - Product/variant mappings with explicit unresolved source facts - A maintenance process for stock, price, product facts and requirements ### We start from - Target shopping channels and current eligibility requirements - Approved product attributes, variants, price and availability - Storefront access, existing feeds and available referral analytics ### From your team Owner - Head of Ecommerce - Head of Growth Involves - Growth - Catalog - Digital product - Engineering - Channel partners Access to these systems and real examples to test against. ### Adapts to - Direct-to-consumer commerce - Marketplace selling - International commerce ### We’d measure - Verified channel-ready product coverage - Rejected records and time to correct changed facts - Observable referred visits and purchases, with unknown attribution kept visible Baselines agreed before we build. ## Questions teams ask. Do we need to replace our current tools? Usually not. We start with the systems you already use and build only what’s missing. Typical connections: Google Merchant Center, analytics, ad platforms, search tools and your product data. Who owns it once it’s running? Your team does. You own the agreed deliverables under the engagement terms, with documentation and training for the people who run it. Ownership usually sits with your head of ecommerce or head of growth. Ongoing support is optional and agreed separately. How do we get started? If the problem and scope are already clear, we can scope this build directly. If you’re weighing it against other priorities, start with Discovery & Action Plan. Its fee is credited in full if we build the recommended work. [How discovery works]() ## Often paired with. Part of [AI shopping & agentic commerce]() [All use cases]() - Catalog & product information ### Supplier data to product records New products wait while teams rekey inconsistent supplier information. Structured product records with source references and exceptions to resolve. [Supplier data to product records]() - Growth & acquisition ### Search performance diagnosis Traffic reports show movement but not a useful explanation or next action. Evidence-linked diagnosis distinguishing demand, conversion and measurement issues. [Mobile product visits fell]() - Digital commerce & conversion ### Guided shopping Complex ranges leave shoppers unsure which product fits their needs. A product-grounded guided-shopping experience with useful next actions. [Guided shopping]() More connected work ## Explore the programs - [Growth & acquisition]() - [Catalog & product information]() - [Digital commerce & conversion]() ## Browse by outcome - [Win more customers]() - [Increase team capacity]() ## Get your products in front of shopping agents. Start with one channel and category. [Discuss this use case]() [**Discuss AI shopping**15 minutes]() [Prefer to start with discovery? →]() We reply within one business day. --- Source: [https://www.thekeating.co/use-cases/ai-shopping-readiness/]() [Contact Keating & Co](mailto:nelson@thekeating.co) · [Agent guidance](https://www.thekeating.co/llms.txt)