Help customers discover what they mean, not just what they type.

No. An ordinary search function often compares mainly the entered words with product titles, categories and descriptions. AI Product Discovery looks more broadly at the intent behind demand. It can combine features, applications, context and related concepts to make an appropriate selection. Search remains an important part, but discovery also includes suggestions, filters, category orientation, and other routes to a suitable product.

The search query is dissected in relevant terms and possible intentions. A question such as “light jacket for cycling in the rain” contains, for example, instructions on product type, weight, use and weather resistance. This information is compared with the available product data. The quality of the result therefore depends not only on the AI model, but also on clear and consistent data on the range.

A usable base consists of clear product names, categories, descriptions and relevant characteristics, such as size, material, color, application and technical properties. Not every product has to have the same fields. More importantly, the characteristics that count for a purchase decision are filled in correctly and consistently. Search data can then show which information is still missing or insufficiently used.

Yes, but the quality of the results follows the quality of the information available. Findoviq can use existing titles, descriptions, categories and attributes while making visible where data is missing. A phased approach is often practical: first support the most important categories and search intentions, then further improve the product data based on actual search behavior.

Then the system should not pretend that there is an exact match. A good no-result experience can show alternative search terms, related categories, or partially appropriate options, provided it remains clear why they are proposed. In addition, the search query can be recorded as a signal for content, product data or assortment. Relevance and trust outweigh showing results at all costs.

Yes. The natural search query may already contain preferences that are interpreted as attributes or filter conditions. After that, the visitor can further refine the selection with visible filters, for example by price, brand, size or availability. AI and filters do not replace each other; together they can shorten the route from a wide need to a concrete product choice.

Yes. Relevance can be combined with business rules, such as inventory, campaigns, seasonal preferences, new products or products that deserve extra attention. This includes staying the basis for the visitor. A promoted product that does not match the search intent harms the experience. Therefore, commercial rules must be verifiable and work within clear boundaries.

No. The value depends mainly on the assortment, the search behavior and the effort that visitors have to find a suitable product. A smaller online store can also have a lot of variation in customer language or products with many characteristics. With a very small and simple assortment, a good category layout can be sufficient. The choice should therefore be based on the actual search queries and bottlenecks, not just on the number of products.

Yes, provided that the chosen languages are well-designed and the product information contains sufficient starting points. Visitors can make the same need different per language. Multilingual support should therefore do more than translate words literally: synonyms, local formulations and product-specific terms are also relevant. Check per language which search questions yield results and where additional terms are needed.

Look not at one digit, but at the full search route. Relevant indicators include the proportion of searches with no usable result, clicking through to products, using filters, additions to the shopping cart and conversion after search. In addition, compare specific search terms before and after a change. This makes it clear whether visitors actually get to suitable products faster, instead of just seeing more results.

Not always. The best approach depends on the current search technique, integration and improvement goals. Product Discovery can become part of a new search experience, but can also be added step by step in addition to existing components. A controlled introduction makes it possible to compare results, rules and measuring points before the application is rolled out more widely.

Good product data, clear relevance limits and tests with real search questions are essential. In addition, category restrictions, attribute rules and merchandising conditions can be used to prevent unwanted combinations. Check especially important, ambiguous and frequently used searches. Search feedback and performance data then help to improve deviations in a targeted manner.