Build AI-powered product recommendation and discovery

Kliv isn’t a recommendation widget or storefront plug-in — it’s an AI app builder that creates software for you.

You describe the app you need in plain words, and Kliv builds it: catalog records, matching rules, search, assistant flows, stock logic, checkout, and customer pages. For commerce, payments run on your own Stripe account. You can change the app later by asking. It isn’t a template.

On this page, that app is AI-powered product recommendation and discovery for a specialty store. Kliv builds all kinds of web apps; this is one example.

Maren runs a hand-dyed yarn label. Shoppers arrive with a project, not a SKU: a worsted baby blanket, machine-washable, enough yardage, and one dye lot. A search bar cannot answer whether a yarn works for that project.

Kliv builds discovery from Maren’s own catalog and rules. Matching can use weight, fiber, care, yardage, dye-lot counts, pattern requirements, substitution rules, and product notes. The store recommends what Maren would defend across the counter.

The recommendation uses inspectable inputs. Attribute rules, semantic search over Maren’s own product notes, and stock counts all come from the catalog. The app can show why an item matched.

Launch pricing can be scheduled. A colorway drop can open at one price and step up later because the catalog says so on a specific date.

The arithmetic can be tested. Yardage matching can be covered by unit tests, so three skeins means three skeins consistently.

Checkout can be rehearsed. Test payments let you run checkout, refunds, and failed cards before real Stripe payments are connected.

The whole direct-to-consumer store — drops, subscriptions, post-purchase email, and more — is covered at the direct-to-consumer solutions page.