The AI Chatbot That Sells Your Catalog

TL;DR: A FAQ bot deflects questions. aiSTAFF reads your product catalog, finds the right items by meaning in any language, shows real product cards, walks a customer through a multi-item cart, and refuses to invent stock you do not carry.

Answering is not selling

A typical store chatbot handles "what are your hours" and stops there. The money question is "I need a comfy chair under 300 lari," and most bots fail it: they match the literal word, miss synonyms, and return nothing. aiSTAFF treats the catalog as a live source of truth and sells from it. If you run an online store in Georgia, start with our AI chatbot development service, then read on for how the selling engine works.

How the bot knows your products

Your catalog is embedded into a private vector store, one per business. Instead of matching exact words, the bot searches by meaning. It blends two methods: dense semantic search (which understands that "couch" and "sofa" are the same idea) and keyword search (which catches exact model names and codes). The two are fused so the best result rises to the top. The mechanics are in why keyword search fails ecommerce chat.

Results are then reranked by how well they match and how popular they are, using rating and review count, so a strong seller outranks a dusty listing. Plurals and word forms are handled, covered in comfy chair should find your armchairs.

It sells your English catalog to a Georgian customer

Many Georgian stores keep their catalog in English. A customer who types in Georgian or Russian would normally get zero results. aiSTAFF translates the query into the catalog language behind the scenes for the search, then replies to the customer in their own language. The shopper never sees the seam. Read how a Georgian customer shops your English catalog.

Real product cards, not a wall of text

When the bot finds a match, it returns a card: name, price, old price and discount, currency, image, category, availability, and rating. The customer sees the item the way they would on a product page, inside the chat. Design and conversion notes are in product cards in chat that convert. The bot also picks context-aware "best" items: ask for cheap and it sorts by price, ask for discounts and it surfaces the markdowns, explained in show me cheap ones.

A multi-item cart inside one chat

Real baskets are not one item. A customer furnishing a room wants a chair, a lamp, and a table. aiSTAFF tracks a shopping list and walks it item by item: browse, discuss, refine, pick, skip, or swap. At the end it summarizes the picks with a running total. The full flow is in the conversational cart. One honest limit: aiSTAFF does discovery and hands off to a contact or callback for the close. It does not take card payment inside the chat.

It also raises the order value the way a good shop assistant does. Buy a hammer and it suggests nails and gloves, drawn from your own catalog, not random upsells. See the bot suggests nails.

It will not invent products

The biggest risk with an AI seller is a confident lie: a product you do not stock, a price you never set. aiSTAFF uses a relevance gate. If a query has no good match in your catalog, the bot returns an honest "we do not carry that" instead of fabricating an item. The threshold is tuned so a search for "gaming mouse" in a furniture store comes back empty rather than wrong. Read the relevance gate.

It checks stock before it promises

A bot that sells an out-of-stock item creates an angry customer and a refund. aiSTAFF checks availability before it confirms, so it never promises what the warehouse cannot ship. The detail is in availability checks.

Keeping the catalog current

Your prices change. aiSTAFF can sync the catalog from your store, including a CS-Cart connection with a resync action, so the bot quotes today's price and today's stock. Setup is in sync your CS-Cart catalog. If you are weighing a chatbot against retraining a model on your data, the comparison is in RAG vs fine-tuning.

What it returns

The value shows up in three places: leads you would have lost after hours, baskets that grew by one or two items, and the manager hours freed from copy-pasting prices. The Georgia-specific math is in ecommerce chatbot ROI for a Georgian store, and a wider view is in the ecommerce chatbot guide.

Shopping by photo

Customers do not always have the word for what they want. They have a picture. A shopper sends a photo of a chair they saw at a friend's place, and the bot reads the image, identifies the type, and pulls the closest matches from your catalog. This turns a vague "do you have something like this" into a product card and a price, without a human staring at the photo. Image input is treated like any other message, so it works on every channel that supports it.

How the bot decides what is best

"Best" depends on what the customer asked. A request for the cheapest option sorts by price. A request for a gift leans on rating and reviews. A request tied to a discount surfaces the markdowns first. The bot reads the intent in the question and ranks accordingly, instead of always pushing the same three items. That is the difference between a recommendation and a search dump, and it is why a well-tuned seller raises the average basket rather than annoying the shopper. The ranking logic is in show me cheap ones.

Beyond a classic store

The catalog does not have to be physical goods. A clinic lists services and prices, a studio lists packages, a school lists courses. Anything you would put in a price list can be embedded and searched the same way. A patient asking about a procedure gets the right service card with the price and what it includes, which is the same machinery that finds a sofa for a furniture shop. The relevance gate keeps it honest in every case, so a service you do not offer comes back as a clear no.

A day in the store

Picture a small electronics shop. A morning customer types in Georgian, "I need a quiet keyboard under 120 lari." The bot translates the query to search the English catalog, returns two mechanical keyboards with low-noise switches, sorted by price, each with a rating and stock status. The customer adds a mouse pad and a wrist rest in the same chat. The bot summarizes the three items with a total, then offers a callback to arrange pickup, capturing a number. Nothing was invented, nothing out of stock was promised, and the basket grew from one item to three.

Where to start

Connect your catalog, set the relevance gate, and turn the bot on for your busiest channel. The selling brain is the same one that answers support, so you are training one worker, not two. Reach us through AI chatbot development to get it live.

Setting it up

The build is short. You connect your catalog, by upload or a CS-Cart sync, and the products are embedded for search. You set the relevance gate so off-catalog queries return empty. You choose the tone and reply length for each channel. Then you test with the messy questions customers send: misspelled model names, vague descriptions, mixed languages. Within an afternoon the bot is searching your real stock and returning cards. As your prices move, a resync keeps the answers current, so the bot never quotes a number you changed last week.

What good looks like

A healthy seller shows three signals. The match rate climbs, because synonyms and other languages no longer return zero. The average basket grows, because the bot suggests the second and third item. The dead-end rate falls, because honest "we do not carry that" replies route the customer to something you do stock instead of a silent failure. Watch those three and you can tell whether the bot is selling or merely chatting. The metrics that matter across chatbots are in chatbot KPIs that matter.

Related reading

• Why Keyword Search Fails Ecommerce Chat
• How a Georgian Customer Shops Your English Catalog
• The Conversational Cart
• The Relevance Gate

FAQ

Can the bot take payment in the chat?

No. aiSTAFF handles product discovery and the multi-item cart, then hands off to a contact or callback to close. It does not process card payment inside the conversation.

What if a customer searches for something I do not sell?

The relevance gate returns an honest "we do not carry that" rather than inventing a product or a price. The bot only sells from your real catalog.

My catalog is in English. Can Georgian customers shop it?

Yes. The bot translates the query into the catalog language for the search and replies to the customer in Georgian or Russian, so the shopper never sees a language gap.

How does it keep prices current?

It syncs from your store, including a CS-Cart connection with a resync action, so it quotes the current price and stock rather than a cached one.