Understanding Search Question
Dissect search queries in product type, brand, application, attributes and conditions. This gives each term the right role before products are picked up.
View floor →AI Search overview
Use AI where it improves understanding of search intent, without losing exact signals.
Good AI Search is not a standalone smart feature. It is a controlled chain that understands customer language, protects exact product information, respects hard conditions and continuously measures quality. Take a look at how each part contributes to this.
From search question to result
The building Blocks of Manageable AI Search
Each building block has its own task. Together, they ensure broad findability without losing relevant codes, filters, product access rights and existing good results.
Recognize search intent
Recognize whether a customer is looking for an exact product, category, application or inspiration. The search strategy then adapts to the probable intention.
View floor →Recognizing concepts
Recognize brands, models, sizes, materials and other product features in free text. This prevents important terms from being treated as ordinary words.
View floor →-Based linking
Find products that fit content, even when the customer uses different words than the catalogue. Exact codes and hard conditions remain separately protected.
View floor →Vector Search
Use meaning representations to find content-related products. Always combine vector distance with metadata, filters, and other search signals.
View floor →-Focused search
Protect exact words, SKUs, EANs, brands and models. -word-oriented search remains the reliable basis alongside meaning-oriented techniques.
View floor →Combined pickup
Combine candidates from exact and semantic search methods. Filters, removal of duplicate results and reordering together determine an explainable final order.
View floor →Hard conditions
Convert size, price, stock, compatibility and customer rights into hard terms. Appropriate meaning is only useful if the product is really suitable.
View floor →Fallback routes
Determine in advance what happens when in doubt, zero-result searches or technical failure. The search function remains usable and predictable.
View floor →Security level
Use security levels to limit automatic interpretations. When in doubt, the search engine chooses a cautious route or asks for clarification.
View floor →Security Limits
Establish boundaries for exact matches, filters, rights and ranking. AI can improve within rules that the team understands and monitors.
View floor →Quality evaluation
Test search quality with real collections of search questions, relevance assessments and regression tests. Measure separately what happens to codes, categories and natural questions.
View floor →Product data
Make titles, categories, attributes and product identifiers reliable and current. Strong product data improves both exact and semantic search results.
View floor →International
Tailor language comprehension, product data and evaluation to every market. Local terms work within the right range and store rules.
View floor →AI Search for B2B
Combine article codes and technical requirements with natural language. Customer rights, contract range and compatibility are always leading.
View floor →Management and responsibility
Arrange ownership, versions, release criteria and surveillance. Every change is demonstrably tested and can be safely reversed.
View floor →Not an opaque system, but a search strategy
Findoviq combines word-oriented search, meaning-based linking, product data and business rules based on the type of search query. Exact search questions receive a different treatment than problem-oriented or inspiring questions. With quality evaluation, security levels, downturn routes and clear management, what is happening and where improvement is needed remains visible.