In complex assortments, a classic search bar is often not enough. The ADLER reference story shows how product knowledge, product data and search intent can be connected so users reach the right product more quickly through use case, material or problem statement.
Product search works well as long as users know the exact product name. It becomes much harder when they can only describe what they want to achieve or which problem they need to solve.
In complex assortments, users often face long result lists, vague matches or abandoned searches even though suitable products do exist. They search by material, surface, property or use case, not by SKU or official product name.
ADLER brought a very concrete setup into the project: an extensive assortment around paints, varnishes, stains and coatings, several digital touchpoints and different audiences across B2C and B2B contexts.
In this context, the AI-Concierge was not treated as an additional chat window. Its role was to connect user questions, website content, product data and assortment logic more effectively.
The search POC was not about a generic AI promise. It focused on observable search quality: interpreting queries, evaluating relevant attributes, ranking suitable products and checking the output against common search situations.
Search catalogues, expected hits and metrics such as top-3 or top-5 hit rates make it possible to validate whether a new search logic produces more relevant results in defined scenarios.
For eCommerce teams, the main value is not another search field but a better use of the product knowledge already available in the business.
Product data, website content and expert knowledge no longer remain isolated in separate systems. They become usable for concrete search and advisory situations. At the same time, the project makes it easier to see which attributes, synonyms, application fields and product groups actually matter.
ADLER illustrates what intelligent product search is really about: not AI as an end in itself, but making existing product knowledge accessible in the way customers actually search.
Anyone who wants to guide users faster towards the right product in complex assortments has to connect product data, expert knowledge and search intent. The ADLER reference makes that connection tangible.
The challenge is not to build more search fields. It is to structure existing knowledge so that it works reliably in real search situations. That is the actual value made visible by this reference.