AI Assistants Recommend Specific Products
ChatGPT Shopping, Amazon Rufus, Google Gemini and Perplexity recommend products. Your product data needs to be readable by these systems for your brand to appear.
ACE · PXM Playbook
PXM Playbook for Agentic Commerce
Missing attributes and inconsistent product facts make your products harder to find. ACE prepares your PXM data for AI-led discovery and retailer search.
55%of shoppers forecast to start product research on LLMs by 2030
25%of global e-commerce forecast to be supported by AI agents by 2030
3.2xconversion lift for products in Walmart's top 3 positions
40%of products fall short on structured-data completeness for LLMs
Product Records Need Facts and Context
LLMs need structured product data with clear meaning and consistent facts. Missing attributes leave gaps in your PXM records. Inconsistent names and missing context make those records harder to interpret.
Start With Product Data
The playbook recommends work on LLM-readable product data in 2025-26, ahead of wider AI-commerce adoption. The approach draws on early organic SEO adoption in the 2010s and applies it to AEO for the 2030s.
One Product Page Supports Both Channels
Amazon Rufus uses the product detail page (PDP) content that supports retailer rankings. Walmart conversational search and Target AI use that content too. The same product data supports LLM discovery.
The 5 Pillars of LLM-Ready Product Data
Attribute Completeness
Complete each retailer's required and recommended attributes. Walmart uses Item Spec 5.0. LLMs use these attributes to form product recommendations; missing facts can leave your product out.
Attribute completeness: 40% of Walmart Polaris ranking weight.
Semantic Content Structure
Write titles and bullets that answer two questions: What is this product? Who is it for? Use factual copy that explains the benefit. LLMs favour that over keyword stuffing.
Content for COSMO and Rufus NLP.
Rich Media Compliance
Match images to retailer specifications and add descriptive alt text. LLMs index that text for visual discovery. Video content raises content quality scores on Amazon and Walmart.
LLMs read image alt text.
Cross-Channel Consistency
Keep the product name consistent between the retailer PDP and your brand website. Check weights and ingredients too. LLMs cross-reference those facts with Google Shopping and social content. Conflicting facts make a recommendation less likely.
Check GTIN accuracy across channels.
Continuous Data Governance
Retailer schemas change, and LLMs retrain. Without governance, a one-time cleanup can lose its effect in 60-90 days. Use agentic monitoring to keep product data current.
Assign an owner to each product category.
Choose Your Service Package
Starter
Starter Audit
One-time fixed fee
Start with a review of your top 50 SKUs before you commit to a full programme.
- Digital shelf audit of your top 50 SKUs on Amazon and Walmart
- Attribute completeness scorecard against retailer schemas
- LLM readability assessment covering semantic and structured-data gaps
- Image compliance check and alt text audit
- 3-page findings report with a prioritised fix list
- 60-minute readout and Q&A session
Programme
PXM Readiness Programme
8-week engagement
Our team applies the full PXM Playbook for Agentic Commerce to your top SKUs.
- Everything in the Starter Audit
- Attribute enrichment and validation for up to 200 SKUs
- Rewritten product descriptions. Titles and bullets are included.
- Product content adapted for Walmart 5.0 and Amazon COSMO
- Consistency audit across brand.com and all 4 retailers
- GTIN and identifier validation across all channels
- Data stewardship workflow and publish gates in your PXM
- LLM discoverability baseline report
- Customised Agentic Commerce Playbook at handover
Retainer
Agentic Commerce Retainer
Monthly fee. 6-month minimum.
Our team uses agentic monitoring to check attribute drift each month. We update your content as retailer schemas change.
- Monthly attribute drift monitoring for up to 500 SKUs
- Content updates as retailer schemas change
- Quarterly LLM discoverability report, with Rufus results alongside those from ChatGPT and Gemini
- Readiness check for each new SKU before launch
- Retailer schema tracking for Item Spec 5.0 and Amazon attributes
- Priority access to the DSIO platform
- Dedicated account manager and quarterly strategy review
8-Week Delivery Roadmap (PXM Readiness Programme)
| Week | Phase | Description | Deliverable |
|---|---|---|---|
| Wk 1-2 | Discovery and Audit | We start with access to your PXM and retailer accounts. An automated audit scores your top SKUs for attribute completeness and semantic quality. It also checks image compliance and cross-channel consistency. | Audit scorecard |
| Wk 3 | Gap Analysis and Prioritisation | We rank gaps by revenue impact and fix high-volume SKUs with the largest content gaps first. Each gap is mapped to a playbook pillar. | Prioritised fix backlog |
| Wk 4-5 | Attribute Enrichment | Our team uses AI to fill attributes for your top 200 SKUs. People review and approve the changes. We write approved attributes back to the PXM master record and validate them against retailer schemas. | Enriched PXM records |
| Wk 5-6 | Content Rewrite for LLMs | We rewrite descriptions so LLMs can interpret the product details. Titles and bullets are included. Retailer-specific versions and revised image alt text go through the same human review queue. | Content rewritten for LLMs |
| Wk 7 | Consistency and GTIN Check | We check all 4 retailer PDPs against brand.com and Google Merchant Center. Our team fixes inconsistent facts and validates UPCs and GTINs on every channel. | Consistency report |
| Wk 8 | Governance and Handover | You receive a customised PXM Agentic Commerce Playbook and an LLM discoverability baseline report. We set up data stewardship and publish gates in your PXM. The handover includes a team training session. | Playbook and governance |
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