Every ecommerce chatbot vendor demo looks similar: a friendly chat window answers a few sample questions smoothly. What separates a real solution from a demo is invisible in that first five minutes — whether the assistant is actually connected to your live catalog, current inventory and real order data, or working from a static script that happens to sound good in a sales call.
That distinction is the whole evaluation. A chatbot that can't see your real stock levels will confidently tell a customer an out-of-stock item is available. One that can't see your actual order system will give a generic tracking link instead of a real answer. The chat interface is commoditized; the integration is where solutions differ.
What a Complete Solution Actually Needs
- Live catalog connection — product details, pricing and availability pulled from your actual product data, not a periodic export
- Real-time inventory — so the assistant never promises stock it doesn't have
- Order management integration — genuine order status and history, not a static "track your package" redirect
- Policy grounding — returns, shipping and promotions answered from your current policies, kept up to date automatically when they change
- Escalation to support — a clean handoff to a human agent with full context when a request goes beyond the assistant's scope
Platform Fit vs Custom Build
If your store runs on a mainstream platform like Shopify, WooCommerce or Magento, a solution with existing connectors for that platform will generally deploy fastest and cheapest — most of the integration work has already been done by someone else. The calculation changes once your stack includes a custom or older ERP, a proprietary order system, or business rules a standard builder can't express; at that point custom development, built specifically around your systems, usually costs less over time than working around a platform's limits.
Evaluation Checklist
| Question | Why it matters |
|---|---|
| Does it read live inventory, or a periodic sync? | Determines whether stock answers are ever wrong |
| Can it complete actions, or only answer questions? | Determines real automation value vs. an FAQ bot |
| What does it cost per conversation at your volume? | Platform fees can exceed a custom build's cost at scale |
| Where is customer data processed? | Matters for privacy policy and regulatory obligations |
| How is accuracy measured after launch? | Determines whether quality is tracked or assumed |
What Vendors Don't Volunteer in a Demo
A sales demo shows the chatbot answering questions it was specifically prepped to answer well, using a curated slice of catalog data. That tells you almost nothing about how it performs on your actual product range, especially the messy parts — discontinued items still showing as active, inconsistent sizing across brands, or promotions that overlap in ways your system wasn't designed to reconcile.
The more useful test is asking a vendor to demo against a sample of your own real catalog and order data, including the awkward edge cases, rather than their prepared examples. How the system behaves when the data is incomplete or contradictory — whether it says so honestly or guesses — reveals far more about production readiness than a smooth answer to a clean question ever will. The same test applies equally to a custom build proposal: ask to see it handle your messiest real SKUs before committing, not just a demo catalog chosen to look clean.
Your customers ask the same questions every day. Let’s automate the answers.
Bring a sample of real conversations — we'll tell you honestly what's worth automating.
Getting Started
The fastest path to a working solution is usually to start with your platform's native integrations and expand from there, rather than building every connection from scratch on day one. For what the assistant should say and do once it's live, see conversational AI chatbots for ecommerce, and for the wider platform this fits into, the conversational AI overview.
Frequently asked questions
What makes a conversational AI chatbot a complete solution rather than just a chat widget?
The integration underneath it. A complete solution is connected to your live product catalog, inventory, order management and store platform, so it can answer accurately and take real actions — not just hold a pleasant-sounding conversation from static information.
Should we buy an off-the-shelf ecommerce chatbot or build a custom one?
If your store runs on a common platform like Shopify or Magento with standard integrations, an off-the-shelf solution can get you running quickly. Custom development makes more sense once you need deeper logic than the platform's builder supports, or integration with systems outside the common platform ecosystem.
How much does an ecommerce conversational AI solution cost?
It depends heavily on integration scope more than the chat interface itself — connecting to a standard platform with existing connectors costs far less than integrating a custom inventory or ERP system. Cost is driven mainly by how much custom integration work the platform and systems require.
Does it work with our existing store platform?
Most solutions integrate with major platforms through their APIs. The key question isn't whether it's technically possible — it almost always is — but how much of that integration is prebuilt versus custom work specific to your setup.
How long does it take to deploy?
A solution built on a standard platform with existing connectors can launch in weeks. One requiring custom integration with inventory, ERP or a proprietary order system takes longer, driven mainly by how much of that connection work needs to be built rather than configured.