Product Feed Optimisation for Agentic Commerce
Make your product catalogue readable, retrievable, and purchasable by AI agents across Google and OpenAI environments.
SearchMinistry helps eCommerce businesses optimise product feeds, conversational attributes, schema markup, variant architecture, and real-time inventory systems for AI-powered commerce across Google UCP, Universal Cart, and OpenAI ACP. If your feeds are not structured for machine retrieval, AI agents will either skip your products or misrepresent them.
See Where Your Feed Is Losing AI Discovery
Tell us about your store and we will identify the highest-impact feed gaps for agentic commerce readiness.
See Where Your Feed Is Losing AI Discovery
Tell us about your store and we will identify the highest-impact feed gaps for agentic commerce readiness.
Trusted by forward-thinking eCommerce brands
AI Agents Are Now Shopping on Behalf of Users
Agentic commerce refers to AI systems that can independently research, compare, recommend, and purchase products on behalf of users. Google has introduced UCP and Universal Cart, while OpenAI has introduced ACP to power conversational AI shopping experiences. These systems do not browse pages the way humans do. They query structured product data, validate attributes, and execute purchases through protocol-based APIs.
What agentic commerce systems rely on
- Structured product feeds with complete attributes
- Machine-readable product relationships
- Real-time pricing and inventory signals
- Semantic product descriptions
- Conversational product retrieval capability
- API-accessible commerce infrastructure
Learn how these systems work in our guide to Google UCP and Universal Commerce Protocol.
Why standard feeds fall short
Traditional product feeds optimised for paid shopping campaigns cover the basics: title, price, GTIN, availability, image. AI commerce systems need significantly more to support recommendation reasoning, follow-up question resolution, variant selection, and autonomous purchasing.
Agentic systems need feeds that support:
- Conversational retrieval and attribute extraction
- Comparison generation across catalogues
- Follow-up question resolution
- Recommendation reasoning
- AI citation selection
Google Feed Optimisation for Agentic Commerce
Google Merchant Center is the core commerce distribution layer for AI-powered product discovery on Google Search AI Mode, Gemini, and Universal Cart. Feed quality is the primary signal that determines whether your products appear in AI-mediated shopping experiences. We optimise both primary and supplemental feeds to meet the full requirements of UCP-based discovery.
Conversational Attributes: The Key Differentiator
Google's optional conversational attributes are the most impactful addition most merchants have not yet implemented. They allow AI agents to answer the follow-up questions that typically block a recommendation or purchase.
question_and_answer
Common pre-purchase Q&A pairs
document_link
Specs, manuals, comparison guides
item_group_title
Product family umbrella name
variant_option
Distinguishing dimension per variant
related_product
Accessories and compatible items
popularity_rank
Relative sales rank within category
OpenAI Commerce Optimisation
ACP (Agentic Commerce Protocol) was introduced by OpenAI to power conversational AI shopping experiences through ChatGPT and OpenAI agents. The key structural difference from Google's approach is that OpenAI recommends a separate, dedicated product feed with per-product declarations for whether each item is searchable and purchasable by AI agents.
Each product in an OpenAI ACP feed requires explicit flags: enable_search: true to allow the agent to surface the product in recommendations, and enable_checkout: true or false to control whether the agent can initiate a purchase for that specific product. This granular per-product control is not available in the Google Merchant Center model.
Who needs an OpenAI ACP feed?
Merchants who sell products that are frequently researched through ChatGPT, or who want to be positioned for conversational commerce on OpenAI-powered surfaces, benefit most from an ACP feed. Because ACP and Google UCP have different specifications, maintaining both feed paths ensures your catalogue is accessible across the full range of AI commerce environments, not just the Google ecosystem.
Real-Time Pricing and Inventory Synchronisation
Agentic AI systems require real-time pricing and inventory accuracy to support autonomous decision-making. An AI agent will not recommend a product that is out of stock, and will not complete a purchase if the price at checkout differs from the price presented during discovery. Traditional batch feed uploads based on spreadsheets or daily exports are not adequate for autonomous purchasing systems.
API-based feed synchronisation
Replace scheduled batch uploads with API-driven feeds that push changes as they happen. Stock level changes, price updates, and availability shifts are reflected in the feed within minutes, not hours.
Event-driven commerce architecture
Trigger feed updates from commerce events: a sale, a stock depletion, a price rule activation. This eliminates the gap between your system of record and what AI agents retrieve.
Automated availability management
Implement incremental feed updates and pricing consistency systems that keep your Merchant Center feed accurate at all times, reducing suppressed listings and improving agent recommendation confidence.
Why batch uploads create problems for agentic commerce
A daily Google Sheets-based feed upload creates a window of up to 24 hours where your feed may show a product as in stock when it has sold out, or at a price that has since changed. For a human browsing a shopping result, this produces a mildly frustrating redirect at checkout. For an autonomous AI agent executing a purchase on behalf of a user, it causes the transaction to fail entirely, and reduces the agent's confidence in your store for future recommendations.
Product Information Management and Schema Optimisation
AI commerce systems evaluate product attributes, compatibility relationships, technical specifications, semantic product categories, and contextual recommendations. Both your feed and your product pages need to expose this information in structured, machine-readable form.
Product Information Management (PIM)
We structure product information across titles, semantic descriptions, attribute completeness, taxonomy alignment, product relationships, and variant architecture so AI systems can interpret your catalogue accurately.
- Product title optimisation for conversational query matching
- Semantic description writing with attribute-level precision
- Attribute completeness review across all variants
- Taxonomy alignment with Google product categories
- Variant architecture and item_group_id implementation
- Semantic product clustering and category consistency
- Related-product and accessory relationship modelling
Schema and Structured Data
Schema.org markup enhances machine understanding by helping AI systems interpret product entities and relationships directly from your product pages, even when agents do not visit the page in a traditional browsing session.
Core entity with name, GTIN, brand, description, image, MPN, SKU
Price, availability, currency, seller, delivery information
Variant families with hasVariant relationships
Return window, method, fees, and conditions
Shipping rates, delivery times, and regions
Customer reviews and overall rating signals
Common pre-purchase questions answered directly on product pages
Governance, Compliance and AI Safety
Agentic commerce systems must operate within regulatory and governance constraints. As AI agents gain the ability to make autonomous purchasing decisions, the data they act on must be governed, validated, and compliant. Feed data errors or policy violations in an agentic context are no longer just a performance issue: they can result in failed autonomous transactions, merchant suspension, or regulatory exposure.
Data governance frameworks
Structured processes for maintaining feed accuracy, attribute completeness, and policy compliance across your full catalogue.
Pricing governance
Automated rules and monitoring to maintain pricing consistency between feeds, product pages, and checkout.
AI-safe automation rules
Feed automation designed to prevent AI agents from acting on stale or inaccurate product data.
Structured audit trails
Logging and documentation of feed changes so you can trace when and why a product's data changed.
Compliance-aware feed management
Feed management aligned with Google's prohibited content policies, return policy requirements, and shipping accuracy rules.
Attribute validation systems
Automated validation of required and optional feed attributes to catch errors before they affect AI discovery.
Service Options
All engagements start with a feed quality assessment. From there, we work in focused sprints or full programmes depending on the depth of work needed and the number of platforms you are optimising for.
Feed Quality Assessment
Best for: Merchants who want to understand their current AI readiness baseline
Timeline: 1 to 2 weeks
- Merchant Center feed audit and error analysis
- Conversational attribute gap report
- Variant and ProductGroup architecture review
- Schema coverage assessment across product pages
- OpenAI ACP feed compatibility check
- Prioritised implementation roadmap
Google Feed Optimisation Sprint
Best for: Merchants with an active Merchant Center account ready for AI improvement
Timeline: 4 weeks
- Primary feed title and description optimisation
- Supplemental feed creation for conversational attributes
- Variant and item_group_id architecture implementation
- Product, Offer and ProductGroup schema implementation
- MerchantReturnPolicy and ShippingDetails schema
- Feed monitoring and error resolution setup
Full Feed and Schema Programme
Best for: Merchants who want end-to-end agent-ready product data across Google and OpenAI
Timeline: 6 to 10 weeks
- Google Feed Optimisation Sprint deliverables
- OpenAI ACP feed creation with enable_search and enable_checkout flags
- Real-time inventory and pricing feed architecture
- Product Information Management (PIM) structuring for AI systems
- Taxonomy alignment and semantic product clustering
- Data governance and compliance framework
- Ongoing feed monitoring and reporting
All pricing on request. Contact us to discuss your catalogue size, platform setup, and commerce goals.
We Combine Feed Engineering with AI Retrieval Expertise
Most feed agencies focus on paid shopping performance. We focus on machine readability, semantic interoperability, and AI retrieval performance across Google and OpenAI ecosystems. Our approach combines technical SEO, structured data engineering, and feed engineering with AI visibility optimisation.
Frequently Asked Questions
A standard product feed is optimised for Google Shopping campaigns: titles, prices, GTINs, availability, and images. An agent-ready feed goes further to include conversational attributes (question_and_answer, document_link, item_group_title, variant_option, related_product, popularity_rank), semantic product descriptions, explicit variant relationships, compatibility attributes, and real-time pricing and inventory signals. AI agents need to answer follow-up questions, compare across catalogues, and validate purchase constraints. A standard feed cannot support those tasks reliably.