Agent-Ready Catalog
Normalize product facts, price, availability, fulfillment and policy information for machine-readable discovery.
← MARTINEZ AI STUDIOS 2.0
AGENTIC COMMERCE
Prepare your business for customers who shop through AI.
We help merchants structure the information and commerce flows AI agents need to discover, understand, recommend and act on products while the merchant keeps control of business rules.
01 / WHO IT IS FOR
E-commerce brands, DTC merchants, specialty retailers and digital businesses with structured products or bookable services.
02 / THE PROBLEM
AI shopping is moving from recommendation toward authorized action. Product details, price, availability, fulfillment and policies need to be understandable and usable by machine-driven experiences, not only by human visitors.
03 / TARGET OUTCOMES
01Cleaner product information for AI discovery
02Clear merchant-controlled policies and rules
03Readiness map for agent-mediated shopping
04Reduced integration ambiguity across new commerce protocols
Concrete use cases.
We start with a bounded outcome. Architecture comes after.
AI Shopping Experience
Design a guided product-discovery flow that can answer, compare and recommend from merchant-controlled data.
Commerce Readiness Audit
Assess the gaps between today’s storefront and a future where external agents participate in discovery and purchase.
Trusted Transaction Path
Map authentication, approval, payment and handoff requirements before exposing transactional capabilities.
04 / WHAT WE DELIVER
A capability, not an empty demo.
- +Agent-readiness audit
- +Catalog/data gap report
- +Discovery and policy architecture
- +Protocol/integration decision map
- +Prototype shopping-agent experience
- +Security and authorization checklist
05 / HOW WE WORK
Audit
Inspect what an agent can accurately discover today.
Structure
Clarify data, policies, inventory and actions.
Prototype
Build a controlled agent-facing commerce experience.
Connect
Add external commerce or payment rails only where the business case is ready.
ILLUSTRATIVE EXAMPLE / NOT A CLIENT CASE
A parts catalog that can answer a precise buying question
Agentic commerce starts with the information a buyer needs to make a decision. Product identity, variants, compatibility and policies must survive a handoff between a product page, a search result and a checkout. A new chat interface cannot repair contradictory stock records.
The scenario
Consider a bicycle parts store. A shopper asks for a component compatible with a particular model and delivery location. The pilot retrieves matching products and explains its evidence; it does not treat a recommendation as permission to charge.
Information needed
- Stable product and variant identifiers, compatibility attributes and clear units.
- A current source for price, stock, delivery and return policies.
- A documented checkout handoff with a review step.
Resolve the exact item
Keep model, size and variant distinct. If compatibility is unconfirmed, ask for the missing identifier rather than substituting a similarly named product.
Explain the offer
Return the product page, evidence for compatibility and the applicable delivery and return rules. State when information is missing or must be recalculated.
Recheck before commitment
Refresh price and stock at checkout. Let the buyer review the item, total, recipient and delivery before authorizing a transaction through the chosen provider.
What a useful handoff looks like
A product recommendation with traceable attributes and a clear route to a reviewed checkout. Machine-readable data agrees with what the human sees.
FROM EXAMPLE TO PILOT
Test before expanding the scope.
Contradictions are visible
Change a variant’s stock between discovery and checkout. The flow must refresh the offer or stop; it must not silently replace the item.
Policies travel with the product
Test a restricted destination and a special-order item. The same restriction should appear in the page, structured data where applicable and purchase review.
What to measure
- Attribute accuracy and agreement between catalog sources.
- Unsupported recommendations and stale offers detected.
- Successful discovery-to-checkout handoffs, with consent tracked separately.
When to stop and review
Being discoverable is not proof of universal agent compatibility. Validate the specific platform and payment path before presenting it as available.
Start with a sample of the current work. Compare equivalent tasks, record human corrections and agree acceptance criteria before attributing an improvement to the system.
What we can learn from other companies.
Public references for studying design decisions. These companies and practitioners are not clients, partners or endorsers of Martinez AI Studios. The studio’s observations are interpretations, not promised results.
EXTERNAL REFERENCE / Shopify
Sidekick: assistance grounded in a store
Shopify describes Sidekick as an assistant that uses store context to help with data analysis, orders, products and content. Its documentation says changes are presented for review. This merchant-facing example is distinct from a customer’s agent autonomously buying across stores.
Our reading: reliable catalog context and reviewable changes matter before a conversational interface can be useful.
Read the source: Shopify Help Center ↗ · Reviewed 2026-09-21
Assess your catalog.
15 questions to identify gaps before planning an integration.
Questions before we build.
Is agentic commerce already real?
Yes, the ecosystem is actively moving from product discovery toward consumer-authorized transactions. Readiness still varies by platform, market and integration.
Do we need to rebuild our storefront?
Not necessarily. We start with the data and actions your current stack can expose safely, then decide what should change.
Will AI agents control our pricing or customer relationship?
They should not by default. The architecture should preserve merchant-defined prices, fulfillment rules, policies, permissions and brand controls.
07 / NEXT STEP
Describe the problem. We design the path.
You do not need to choose models, frameworks or protocols. Tell us the process, the friction and the outcome you want.