AI agents are becoming a product discovery channel
ChatGPT Shopping, Amazon Rufus, Google Gemini, and Perplexity can recommend specific products, making product-data readability increasingly important.
Prepare Product Data for Agentic Commerce
Improve Discoverability Across LLMs and Retailer Search
ACE is GSPANN's PXM playbook for agentic commerce, helping brands make product data structured, semantically rich, consistent, and discoverable across LLMs, AI agents, and retailer search.
AI shopping experiences now recommend specific products. Incomplete, inconsistent, or hard-to-interpret product data can reduce a brand's visibility across these emerging discovery channels.
Key Capabilities
The five pillars of LLM-ready product data
Attribute Completeness
Fill required and recommended attributes against retailer schemas so LLMs and retail search have the product facts they need to form recommendations.
Foundation: complete, retailer-ready product records
Semantic Content Structure
Rewrite titles, bullets, and descriptions in natural language that clearly explains what the product is, who it is for, and why it matters.
LLM readability: factual, benefit-led content
Rich Media Compliance
Align images with retailer specifications, improve descriptive alt text, and strengthen visual content for AI-led product discovery.
Visual discovery: structured image and media signals
Cross-Channel Consistency
Keep names, weights, ingredients, identifiers, and other product facts consistent across retailer PDPs, brand.com, Google Shopping, and other channels.
Trust signal: consistency across every product surface
Continuous Data Governance
Monitor attribute drift and retailer schema changes so product data stays current instead of reverting after a one-time cleanup.
Maintenance: ongoing ownership and publish governance
Why It Matters
Product discovery is moving beyond the traditional search bar
55%
of shoppers will start product research on LLMs by 2030
25%
of global ecommerce will be enabled by AI agents by 2030
3.2x
conversion lift for products in Walmart top 3 positions
40%
of products fail structured data completeness for LLMs
Most PXM data was not built for LLM discovery
Incomplete attributes, inconsistent naming, and missing context make it harder for AI systems to interpret products confidently.
Retailer search and LLM discovery are converging
The same PDP content supports organic retailer ranking and AI-led discovery, allowing brands to improve both from a stronger product-data foundation.
Readiness requires continuous governance
Retailer schemas change and product data decays. A durable approach needs monitoring, stewardship, and controlled publishing—not just a one-time cleanup.
Engagement Options
Three ways to engage
Starter Audit
Understand where your product data stands before committing to a broader programme.
- ✓ Digital shelf audit for top 50 SKUs on Amazon and Walmart
- ✓ Attribute completeness scorecard against retailer schemas
- ✓ LLM readability and structured-data gap assessment
- ✓ Image compliance and alt-text audit
- ✓ Prioritised findings report and readout
PXM Readiness Programme
Hands-on optimisation of priority SKUs using the full PXM playbook for agentic commerce.
- ✓ Everything in the Starter Audit
- ✓ Attribute enrichment for up to 200 SKUs
- ✓ Semantic rewrite of titles, bullets, and descriptions
- ✓ Retailer-specific content variants
- ✓ Cross-channel consistency and GTIN validation
- ✓ PXM governance framework and publish gates
- ✓ LLM discoverability baseline and customised playbook
Agentic Commerce Retainer
Ongoing monitoring and continuous optimisation as retailer schemas and AI commerce evolve.
- ✓ Monthly attribute-drift monitoring for up to 500 SKUs
- ✓ Continuous content optimisation
- ✓ Quarterly LLM discoverability reporting
- ✓ New-SKU readiness checks before launch
- ✓ Retailer schema update tracking
- ✓ Priority access to the DSIO platform
- ✓ Dedicated account management and quarterly strategy review
Make your product data easier for AI and retail search to understand
Start with a focused readiness audit to identify attribute, content, rich-media, consistency, and governance gaps across your priority products.
Insights
