Ask a typical retail chatbot: "I'd like to buy the blue jacket in my size."
It will give you a link.
Ask Cartivo the same thing. It will check your size from your profile, confirm the jacket is in stock, add it to your cart, and ask how you'd like to pay.
That difference — a link versus a completed transaction — is the entire gap between AI that impresses in a demo and AI that shows up in a revenue report.
The Three Tests
The AI-in-retail category is crowded with everything from genuinely capable systems to FAQ bots with a coat of GPT paint and a friendlier name. Here's a framework for telling them apart. Ask the vendor to demonstrate three things:
Can it transact?
Not 'can it show products.' Can it add to cart? Can it apply a promo code? Can it trigger a checkout? If the answer involves a redirect to another page, you're looking at a search tool with a conversation UI. A real commerce agent completes the transaction in the conversation.
Cartivo adds to cart, applies promotions, and completes checkout — in the conversation
Chatbot: returns product links, redirects to cart, opens a new tab
Does it know who the customer is?
Authentication is the hardest part of commerce AI that nobody talks about. Any tool can retrieve a product. But can the agent log the customer in — via OTP, social login, email and password — and then retrieve their cart, their order history, their saved addresses and payment methods? If authentication requires leaving the conversation, you lose the customer. Every time.
Cartivo handles OTP, Google, Facebook, and email+password inside the conversation
Chatbot: redirects to login page, breaking the conversation flow
Does it understand failure?
What happens when the item is out of stock? When the promo code has expired? When the preferred size isn't available but a sister product is? A real agent handles these gracefully with actionable alternatives. A chatbot returns an error or, worse, hallucinates availability.
Cartivo reads live inventory and offers real alternatives when the first choice fails
Chatbot: says 'Sorry, I couldn't find that' or shows a product that's actually out of stock
The architecture distinction that explains everything.
Most "AI shopping assistants" are large language models with access to a product catalog API. The LLM talks; the catalog answers. That's a retrieval-augmented generation (RAG) pattern, and it's genuinely useful — for answering questions.
Commerce is not an answering problem. It's a transaction problem.
Completing a purchase requires orchestrating multiple systems in sequence:
What a real commerce agent must orchestrate
Each of these is a separate API call. Each has error conditions. Each must be orchestrated correctly, in sequence, with the AI maintaining conversational context throughout.
This is what makes a genuine commerce agent hard to build — and what separates the real ones from the chatbots.
The white-label layer is harder than it sounds.
Here's a problem that sounds simple and isn't: making the same AI behave like a Rivoli luxury watch consultant in one deployment and like a Leslie's pool chemistry expert in another.
This isn't prompt engineering with a different system message. It's behavioral calibration — ensuring the agent's vocabulary, tone, depth of expertise, upsell approach, and handling of ambiguous requests matches the brand and the category.
Rivoli Luxury Agent
"That's a distinguished selection — the Longines Master Collection has exceptional movement reliability for its price point. May I ask about the occasion? That will help me suggest the right case size."
Leslie's Expert Agent
"Let's start with your current chlorine reading — that'll tell us exactly what we're dealing with. What does your test strip show for free chlorine and pH right now?"
Same underlying platform. Radically different personalities. The agent has to actually understand the domain — not wear a costume.
On the "runs on your platform" problem.
Every major retail brand has a commerce platform they've invested years and millions into — Salesforce Commerce Cloud, commercetools, Shopify Plus, Shopware. The worst AI integrations require ripping it out. The mediocre ones bolt on as a disconnected layer. The right ones integrate natively — using the commerce platform's own APIs to read, write, and transact.
This is non-trivial engineering. SFCC's SCAPI, commercetools' APIs, Shopify's Admin API — each has its own authentication model, data model, and rate limits. When a retailer goes live with Cartivo on their SFCC instance, Cartivo doesn't know about SFCC. It knows about the customer in front of it. The platform adapter handles the translation.
What "deployed to your cloud" actually protects.
Commerce conversations contain sensitive data: customer intent signals, purchase history patterns, payment interactions, behavioral data at a granular level.
When this data flows through a vendor's shared SaaS infrastructure, you have a third-party data relationship whether you've negotiated it or not. Your customers' conversations — and the behavioral patterns they reveal — are touching infrastructure you don't control.
The right deployment model runs the AI agent inside the retailer's own cloud account. Network calls never leave the retailer's perimeter. Conversation logs are the retailer's. This is harder to build than SaaS. It's also the only defensible model for a brand that takes customer data seriously.
A test I'd invite.
If you're evaluating AI shopping tools right now, run this test on every vendor, including us:
Give the agent a natural language shopping request with a constraint ("I need a wedding gift for a watch lover, budget AED 2,000, needs to be delivered before Saturday"). Watch what it does.
Does it show you results? Or does it close the sale?
The vendors who can close the sale in that conversation — not just show the products — are the ones worth evaluating seriously.
We'll run this demo live, on a real catalog, any time.