Trusted by 10,000+ teams
Help shoppers find the right product faster
Turn every BigCommerce conversation into insight that helps you fix friction and drive more sales.
Explores your live catalog
The agent searches the same BigCommerce catalog your storefront runs on, then shows shoppers the products, variants, prices, and stock status they can actually buy right now.
Product-card answers
Turns a broad request into browsable product cards with images, prices, availability, and the next best action inside chat.
Variant-aware
Reads options like size, color, material, and SKU before it recommends an item or confirms a specific version.
Stock checked first
Checks live inventory before answering, so sold-out items stay out of the conversation instead of creating cleanup work later.
Searches in batches
Looks up several products in one pass, so a shopper listing four things they need gets one answer rather than four rounds of back-and-forth.
Takes the cart all the way
Discovery doesn't stop at a recommendation. The agent adds the chosen variant behind a confirmation step, adjusts what's already in the cart, and hands over a checkout link without moving the shopper to another page.
Confirmed add-to-cart
Adds the exact variant the shopper picked, behind a confirmation dialog, so nothing lands in the cart by accident.
Quantities and removals
Changes counts or drops an item straight from the conversation, instead of sending the shopper back to a cart page to do it.
Checkout on request
Hands over a checkout link the moment the shopper is ready, so the path from question to purchase stays in one place.
Related items alongside
Surfaces the natural add-on on the card and again in the cart, at the two moments one more item is still an easy yes.
Turns questions into store signals
Conversation data becomes a working view of what shoppers search for, where checkout confidence drops, and which product details need clearer answers.
Demand patterns
Shows recurring product searches and feature questions, so your team can see what shoppers are trying to find.
Drop-off clues
Highlights hesitation around shipping, returns, sizing, or price before those moments quietly turn into lost carts.
Content gaps
Points at the product pages and policies people keep asking about, which is usually where the description is doing too little work.
Feedback worth acting on
Turns raw chat volume into concrete themes your team can act on, rather than anecdotes nobody has time to dig through.
Shoppers get help without waiting on your team
They get the product, the answer, or a callback in the same chat. You only join when a person should take it from there.
Your BigCommerce cart, campaigns, and control
Checkout is the store checkout, related products come from the catalog you already sell, and campaigns and guardrails stay in your hands.
Real cart and checkout in chat
Shoppers see line items, change quantity, and go to your actual BigCommerce checkout. This is the store cart, not a widget-only basket.
Trained on your store
Crawl your pages, policies, and brand copy. The catalog is live BigCommerce data. The writing is yours.
Same agent on every channel
Store, WhatsApp, and Instagram run the same agent and the same training. Cart actions stay on the BigCommerce storefront.
Recommends related products
Shows a related-products strip on the card and in the cart, so the extra is one you already sell.
Campaigns that reach shoppers first
You start the conversation on WhatsApp, SMS, email, or phone. When they reply, the same BigCommerce agent continues with the catalog already there.
Self-learning
The agent gets better from real store conversations. Missed questions become training you approve, so the next shopper gets a stronger answer.
Product-Aware AI
Shoppers open the product in the chat. They see photos, pick a size or color, and add it without going to another page. Out of stock is marked and cannot be added to the BigCommerce cart.
You enable the store actions
Search, product cards, cart, and checkout run on a Stencil storefront. You decide what the agent can do.
Guardrails you set
Persona, limits, and what it must not do are yours. The agent works inside that, not around it.
Multi-agent system
You set up specialized agents for sales, support, and catalog. The right one takes the question with the BigCommerce cart already in view.
Live without writing code
Paste one script in Storefront Script Manager. No marketplace app and no API token to rotate.
Try it on your store
Add the script to your BigCommerce store and watch cart, related products, campaigns, and learning run on your real catalog.
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ISO 27001
No training on user data
Data Isolation
SSO Support
Model providers configured with privacy-first settings, including zero‑day retention.
Frequently Asked Questions
Check out the most common questions and answers below
What can YourGPT do on a BigCommerce store?
Does YourGPT charge per resolution on BigCommerce?
Can YourGPT hand a BigCommerce chat to a human?
Yes. YourGPT hands the conversation to your team with the full thread already there. The human does not open a blank ticket and ask the shopper to repeat themselves. They start on the part that needs a person.
This is built for your team as well as the agent. Your support staff stay faster because the context is already in front of them. You stay in control of when that happens.
Can shoppers add to the real BigCommerce cart from YourGPT?
Yes. A YourGPT BigCommerce AI chatbot can add the chosen variant to the shopper's real BigCommerce cart from the conversation, behind a confirmation step. This is the same cart they would see at checkout, not a separate basket inside the chat.
YourGPT can search the catalog, show the product in chat, open options like size and color, change quantities, and send a checkout link. Sold-out items cannot be added.
Can YourGPT recommend products on a BigCommerce store?
Does YourGPT guess product answers on BigCommerce?
No. YourGPT does not guess product answers. Variants, stock, and price come from the live BigCommerce catalog. Policies come from your store pages. You control the training and what the agent is allowed to do.
General language models guess from what they were trained on. They do not read your BigCommerce catalog unless you build that around them, so size, stock, and price can be wrong.
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