Search Functionality Examples - Inspiration & Best Practices

Powerful search functionality can make the difference between an application that frustrates users and one that leads them directly to the right result. From e-commerce product search to internal knowledge bases — users expect fast, relevant search results with smart suggestions and filters. Modern search technologies combine full-text search, faceted filtering, and AI-powered semantic matching. In these examples, we show how organisations have transformed their search experience.

E-commerce product search with faceted search

A fashion webshop with 50,000+ products implemented advanced search functionality using Elasticsearch. Customers search by keywords and then filter by brand, size, colour, price, and availability. Typo tolerance ensures search results remain relevant even with spelling mistakes. Autocomplete suggestions appear as the user types, accelerating the search process and increasing conversion by 25%.

• Elasticsearch backend for millisecond response times across 50,000+ products
• Faceted filtering by brand, size, colour, price, and availability
• Typo-tolerant fuzzy matching for improved search results
• Real-time autocomplete suggestions while typing

Internal knowledge base search for a consultancy firm

A consultancy firm with 10,000+ documents built an intelligent search function for their internal knowledge base. The search engine indexes PDFs, Word documents, presentations, and wiki pages, offering full-text search with relevance ranking. AI-powered suggestions show related documents and internal experts who have knowledge about the searched topic.

• Multi-format indexing of PDF, Word, PowerPoint, and wiki pages
• Relevance ranking based on TF-IDF and recency boosting
• AI-powered suggestions for related documents and internal experts
• Search analytics for insight into popular search terms and knowledge gaps

Geospatial search for a real estate platform

A real estate platform implemented map-based search functionality where users search for properties by location, radius, and neighbourhood characteristics. The search engine combines geospatial queries with traditional filters on price, area, and property type. Users draw a search area on the map and receive automatic notifications when new properties matching their profile are added.

• PostGIS-based geospatial queries for location-based search
• Draw-on-map functionality for custom search areas
• Combination of geospatial and traditional filters in one query
• Automatic alerts for new matches within the search profile

AI-powered semantic search for a legal database

A legal publisher implemented semantic search for their database of 100,000+ legal documents. Instead of only matching keywords, the search engine understands the meaning of the query and finds conceptually relevant results. A lawyer searching for 'tenant refuses to pay' also finds documents about 'lease payment default' without those exact words appearing in the query.

• Vector embeddings for semantic understanding of legal texts
• Hybrid search: combination of keyword search and semantic matching
• Domain-specific language model trained on legal terminology
• Citation linking between related case law

Key takeaways

• Typo tolerance and autocomplete are baseline features that drastically improve the search experience.
• Faceted filtering gives users control over their search results and increases conversion in e-commerce.
• Semantic search with AI goes beyond keyword matching and finds results based on meaning.
• Search analytics reveal what users search for and don't find, providing valuable insights for content and product strategy.

How MG Software can help

MG Software builds search functionality that leads your users directly to the right result. From Elasticsearch implementations with faceted filtering to AI-powered semantic search engines — we choose the right technology for your use case and continuously optimise based on search analytics and user feedback.