Right product. Right price.
A cart that works.
RetailAgentOS helps AI shopping assistants understand each retailer’s products, prices and purchase rules—before checkout.
For one store or thousands.
- Same question, same answer
- Every decision explained
- Open specifications
- Tested reference engine
No jargon
Different businesses. One shared problem: AI does not understand their commerce rules.
Choose the perspective closest to yours. We’ll explain the value without protocols, specifications or architecture diagrams.
Enterprise Retail Leaders
Keep every AI shopping channel aligned with your retail policies.
RetailAgentOS gives AI agents consistent answers across pricing, customer eligibility, inventory and fulfilment.
- Govern how AI represents the business
- Reduce inconsistent prices and fulfilment promises
- Explain and audit every decision
Independent & Specialty Retailers
Help AI find your products—and represent your store correctly.
Make your products, prices and delivery rules understandable to AI shoppers without rebuilding your storefront.
- Become visible to AI shoppers
- Show the correct customer price
- Prevent impossible orders
Commerce & Fulfilment Platforms
Make AI commerce reliable across thousands of different merchants.
Turn merchant-specific rules into consistent answers that shopping agents can use before creating a cart.
- Reduce invalid and failed carts
- Lower merchant-specific exception logic
- Accelerate merchant and AI-agent adoption
Building the infrastructure? Explore the open specifications, reference engine and live playground.
For developers →One transaction, start to finish
Here is what happens behind a single shopping request.
buyer query
“Find groceries for tonight under $80, use my membership price and only include items deliverable to my address.”
- 1
A shopper asks an AI
For something specific — a product, a price limit, a delivery need.
- 2
The AI discovers a retailer
It finds a retailer and product that could match the request.
- 3
RetailAgentOS evaluates the rules
It checks that retailer's eligibility, pricing, inventory and fulfilment rules.
- 4
It returns a decision and a reason
A clear answer — not a guess — with an explanation attached.
- 5
The AI builds a cart that can proceed
No dead-end orders, no checkout surprises.
What RetailAgentOS decides
One decision layer. Very different kinds of retail.
The rules change by retailer. The need for a reliable answer does not.
Sara's Boutique
AI shopping assistants never recommend her handcrafted products — she has no machine-readable way to declare what she sells or who it’s for.
She declares her catalog once. Any agent helping a shopper find personalised gifts now finds her.
B&T Wholesale
Tiered pricing and buyer-qualification rules are invisible to agents. Buyers get quoted the wrong price, or see listings they can’t purchase.
RetailAgentOS enforces qualification gates and volume pricing automatically. Agents always quote the right tier to the right buyer.
Fresh Corner Market
Agents route buyers toward fulfilment modes the store can’t support, and weekly promos aren’t visible at browse time.
RetailAgentOS surfaces active promo pricing and flags unsupported fulfilment modes before the buyer wastes a trip.
A translator between commerce systems and AI shoppers
The store remains the source of truth. RetailAgentOS makes that truth usable by AI shopping agents.
The retailer publishes what it can support.
Prices, buyer eligibility, inventory and fulfilment rules — declared once, in a form machines can read.
The shopping agent provides the customer and transaction context.
Who is buying, where, and what they are trying to do.
RetailAgentOS returns a consistent answer with a reason.
The same question gets the same answer, every time — and every answer explains itself.
Built in the open. Clear about what is real.
328/328 automated tests currently pass against the reference engine. Every claim on this page is checked against that same evidence.
16
Built and tested
Eligibility, contextual pricing, inventory, quote integrity and the trust/provenance envelope all run against a tested reference engine.
1
In pilot or partially built
Fulfilment feasibility exists for mode and region checks today; delivery windows and lead times are still being designed.
5
Designed or planned
Promotion stacking, loyalty and restricted-goods enforcement are specified but not yet implemented.
Want the technical detail?
Explore the open specifications, reference engine, decision model and live playground.
You’ve seen the problem. Pick your path.
Wherever you sit — retailer, enterprise, platform or developer — there’s a next step that matches how you evaluate this.