Your catalog is fuel for AI: preparing product data that sells
When a bot answers poorly, the cause is rarely “dumb AI” — it's usually gaps in the data. The bot can't say whether a jacket is waterproof if you never wrote it anywhere. The good news: the same fixes that feed the bot also improve SEO and product-page conversion.
Descriptions: write answers, not poetry
Customers (and the bot on their behalf) ask concrete questions: dimensions, material, compatibility, “what does it go with”, “who is it for”. Review your bestsellers and check whether the description answers the five most common questions about that product — if not, that's the first fix, before any AI deployment.
Where to get those questions? From your emails and customer conversations. And after deploying a bot — from its logs: every question the bot couldn't answer from the description is a ready-made item for the fix list.
Variants, stock and prices: one source of truth
The bot must know that “red M” is in stock and “black L” isn't — otherwise it recommends a product the customer can't buy. Data must flow from one source: the store API or a feed, synced automatically. A manually uploaded file from a month ago is a recipe for a lying bot.
The feed you already send to comparison engines (Google Merchant) is usually good enough to start — one more reason to keep it in shape.
Store policies are data too
Delivery, returns, complaints, fulfillment times — these are data just like prices, and questions about them kill the most carts. Write them down briefly and unambiguously (amounts, deadlines, exceptions), in one place. The bot gets them as rules, customers get them as an FAQ page, and you stop answering the same question for the fifth time this week.
A closing test: ask someone outside the company to answer your ten most common customer questions using only your website. Wherever they fail, the bot would fail too — and that's exactly where to start.
CartBot for your platform: WooCommerce · Shopify · PrestaShop · Shoper