A chatbot in an online store is only as good as the way it finds products. Ask a store assistant for "a lightweight rain jacket for running, under 100" and you expect two or three concrete suggestions with a price and a link. Whether the chatbot can deliver that depends far less on the AI model and far more on where its product knowledge comes from and how fresh it is.
There are three ways to give an e-commerce chatbot access to your products:
- Website scraping - the chatbot is trained on a scanned copy of your store pages.
- Store API search - the chatbot passes the customer's phrase to your e-commerce platform's search endpoint.
- AI Search - the chatbot works on a product knowledge base built from your catalog, finds products by meaning and checks live prices and stock in your store at the moment of the answer.
This article explains how each approach works, where it shines, where it fails and how to choose. If you only want the summary, jump to the comparison table.
Approach 1: Training on scanned website pages
This is the classic way to build a chatbot. ChatLab crawls your store, extracts the visible text of every page and stores it in the knowledge base. Product pages, category pages, delivery terms, size guides and FAQs all become training data. A detailed setup guide is in Training a bot on an e-commerce store without an integration.
How it finds products: the customer's question is compared with fragments of scanned pages. The chatbot gets the fragments that look most similar and writes an answer based on them.
Advantages
- Works with any store on any platform, no integration or feed required.
- Captures everything a page says: long descriptions, materials, care instructions, comparison tables, buying guides.
- The only option for content that is not a product at all: policies, FAQs, brand story, blog articles.
Disadvantages
- Outdated prices and stock. The chatbot remembers the page as it looked at training time. A price change, a promotion or a sold-out product is invisible until the next retrain. A customer who hears one price in the chat and sees another in the cart loses trust in both.
- Noise instead of product knowledge. A product page carries a menu, footer, cookie banner, "customers also bought" widgets, reviews and newsletter forms. Without careful element filtering all of it lands in the training data and competes with the product itself for the chatbot's attention.
- Too much text, too little structure. The chatbot sees prose, not fields. It does not know that "299" is the price and "S, M, L" are variants, so it cannot filter by budget or size, only guess from text.
- Character limits. Large catalogs quickly exhaust the training character limit of your plan, and the whole catalog must be rescanned to pick up changes.
- Weak recall on large catalogs. With thousands of similar products the most similar text fragments are often not the best products, and the chatbot tends to repeat the same few items.
Best for: small catalogs that rarely change, stores where the chatbot mostly answers questions about service and policies, and as the informational layer next to a live product source.
Approach 2: Product search through the store API
When your store runs on a platform with a native ChatLab integration (Shopify, WooCommerce, PrestaShop, Shoper, BaseLinker, CS-Cart, Wix and others), the chatbot can call the platform's API. The basic product search action sends the customer's phrase to your store's search endpoint and presents what comes back as product cards.
How it finds products: exactly the way your store's own search box does. The chatbot extracts a phrase from the conversation, the platform matches it against product names and sometimes descriptions, and returns the hits.
Advantages
- Always live. Prices, stock, variants and product links come straight from the store at the moment of the question.
- No training and no character limits. The catalog is not copied anywhere, so its size does not matter.
- Great for exact lookups. Product names, SKUs and codes are found instantly.
- Comes with the rest of the integration: order status, add to cart, in-stock filtering.
Disadvantages
- Keyword matching, one to one. Most platform search endpoints look for the literal words. "Sneakers for wide feet" finds products only if a name or description contains those words. Synonyms ("trainers", "running shoes"), typos, other languages and descriptive needs return nothing or the wrong items.
- The chatbot has to guess the phrase. From "something warm for my daughter, she is 8" the AI must invent a search term your store engine will accept. Every guess that misses costs a round trip and a message credit, and the customer sees a "no results" answer.
- No understanding of budget or purpose. "Under 200" or "for a beginner" are not search terms. Filtering usually depends on what the platform endpoint supports, and often it supports very little.
- Quality depends on the platform. Some store search engines are excellent, some match only titles. The chatbot inherits every limitation.
Best for: stores where customers know what they want and search by name or code, and as the live data layer for order status and cart operations.
Approach 3: AI Search (product knowledge base with live data)
AI Search combines the strengths of the two approaches above. ChatLab reads your catalog through the integration or a product feed and builds a dedicated product knowledge base: every product with its name, full description, attributes, categories, tags, codes (SKU, EAN), price and images. The catalog is refreshed on a schedule and only changed products are re-indexed. Product pages, documents and FAQs are kept separately in the regular training data and do not count against the product limit.
How it finds products: the customer's question is matched against product meaning, not words. "A warm but light jacket for hiking in autumn, under 300" is understood as a category, a use case and a budget. AI Search narrows by price and category, ranks the products that fit the description, and then reads the current price, stock, sizes and variants from your store before the answer is shown. Codes still work as exact matches, so "is 5901234123457 in stock" is answered instantly.
Advantages
- Contextual search. Synonyms, typos, other languages, descriptions of needs and comparisons ("something like X but cheaper") all lead to the right products.
- Live prices and stock. The knowledge base finds the products, the store delivers the numbers. The customer never hears a stale price.
- Filters that customers actually use. Price range and category are applied as filters, not guessed as keywords.
- Recommendations, not just lookups. When the exact item is sold out or does not exist, the chatbot proposes alternatives that match the same need.
- Clean product knowledge. Only product data is indexed, without menus, banners or "you may also like" widgets. Nothing from the catalog uses up your training characters.
- Scales with the catalog. Thousands of products are searched as fast as ten, and a refresh only touches products that changed.
- Works without a native integration through a Google Merchant XML feed, so any store can use it. See Integration with XML feed.
Disadvantages
- Requires an integration or a product feed and an AI Search product pack sized for your catalog.
- New products appear in the chatbot after the next scheduled refresh, not the second they are published. Prices and stock of already indexed products are still read live.
- Feed-based setups read prices and availability from the feed, so their freshness equals the feed refresh interval, and order status or add to cart need a native integration.
Best for: any store where customers describe what they need instead of typing an exact name, catalogs with hundreds or thousands of products, and stores that want the chatbot to sell, not only to answer.
Comparison table
| Website scraping | Store API search | AI Search | |
|---|---|---|---|
| Source of product knowledge | Static copy of scanned pages | Live call to the platform search endpoint | Indexed product catalog plus live store read |
| How matching works | Text similarity on page fragments | Keyword match, usually one to one | Semantic match on product meaning |
| Understands "a warm jacket under 300" | Partly, guesses from text | No, needs literal keywords | Yes, category, use case and price filter |
| Synonyms, typos, other languages | Sometimes | Rarely | Yes |
| Prices in answers | As of the last training | Live | Live from the store (from the feed for XML setups) |
| Stock and variants | As of the last training | Live | Live from the store (from the feed for XML setups) |
| Price and category filters | No | Depends on the platform | Yes |
| Exact search by SKU or EAN | Only if the code is on the page | Yes | Yes |
| Alternatives when sold out | No | No | Yes |
| Noise from menus, banners, widgets | High without element filtering | None | None |
| Uses training characters | Yes, the whole catalog | No | No, products are counted separately |
| Large catalogs | Hits character limits, weaker recall | Fine | Fine, refresh is incremental |
| New product visible in chat | After a retrain | Immediately | After the next scheduled refresh |
| Order status, add to cart | No | Yes, with a native integration | Yes, with a native integration |
| Setup effort | Scan and filter pages | Connect the integration | Connect the integration or a feed, assign a product pack |
| Requirements | Any website | Native integration | Native integration or XML feed plus AI Search product pack |
Which approach should you choose?
- You sell a few dozen products and the chatbot is mainly there for delivery, returns and FAQs. Website scraping is enough. Follow the element filtering guide so product pages come in clean.
- Your platform has a native integration and customers search by product name or code. The integration's product search action gives live data with zero setup beyond connecting the store. Add AI Search when you notice "no results" answers for descriptive questions.
- You have hundreds or thousands of products, customers ask in their own words, and you want the chatbot to recommend and upsell. AI Search is the right choice. It is the default in every ChatLab e-commerce plan.
- Your platform has no native integration. Export a Google Merchant XML feed and use AI Search over it. You get contextual search and product cards; order status and cart operations stay unavailable.
In practice the best setup is layered: informational pages through scraping, the catalog through AI Search, and orders and cart through the native integration. Each source does the one thing it is good at.
How to enable AI Search in ChatLab
- Open your chatbot and go to the Connect tab. Connect your store with a native integration, or add an XML feed if your platform is not on the list.
- Make sure the chatbot has an AI Search product pack that covers your catalog size. Every e-commerce plan includes a number of indexed products, and bigger catalogs add product packs.
- Wait for the first indexing to finish. The indexed catalog is listed under Training > E-commerce catalog on the Products and Categories tabs, where you can check what the chatbot can find.
- In Settings > Actions, confirm that the AI product search action of your integration is enabled. When AI Search is active it replaces the plain phrase search, so the chatbot always uses the smarter path.
- Turn on Products View in Settings > Model & Advanced so results appear as product cards with an image, price and link.
- Keep AI Search auto-training enabled in the chatbot's Advanced settings, so the catalog is refreshed on schedule and only changed products are re-indexed.
Then test it the way a customer would: describe a need instead of typing a name, add a budget, ask for something similar but cheaper, and ask about an item you know is sold out. A well-configured AI Search answers each of these with concrete products and current prices.