AI-Driven Search - Full-Text, Semantic and Hybrid Search

Traditional search matches on exact keywords. AI-driven search understands intent, synonyms, and context — and delivers more relevant results. By combining embeddings and vector databases with classical full-text search, you get hybrid search solutions that significantly improve user experience. Discover how businesses apply this.

E-commerce product search with semantic and faceted search

An online store with 50,000+ products implemented an AI search that understands search terms semantically. "Red leather sofa" finds "burgundy leather sofa" and products with similar descriptions. Faceted filters (material, price, brand) work seamlessly with the semantic layer. Conversion from search results increased by 35%.

• Embedding-based semantic search ranking alongside keyword matching
• Synonyms and query expansion for better recall
• A/B testing of search algorithms for continuous optimisation

Enterprise knowledge search for internal documents

A consultancy firm built an internal search engine over their knowledge base, project documents, and case studies. Employees search in natural language ("examples of pricing strategies for SaaS") and get ranked results with snippets and source references. Time to find relevant information decreased by 60%.

• Vector database with document embeddings for semantic similarity
• Hybrid ranking: keyword + vector similarity for precision and recall
• Access control per document for result filtering

Support ticket search with question-answer matching

A SaaS company improved their support tool with AI search. Support agents type a customer question and instantly get similar solved tickets, articles, and code snippets. This reduces duplicate work and significantly speeds up resolution time.

• Question-answering model for query-document matching
• Real-time indexing of new tickets and knowledge base updates
• Highlighting of relevant passages in results

Key takeaways

• Hybrid search (keyword + semantic) often delivers better results than purely semantic or purely keyword — it combines precision with recall.
• Query understanding (reformulation, spellcheck, intent) has a major impact on user experience.
• Faceted filters and personalised ranking further increase relevance when many results are possible.

How AVARC Solutions can help

AVARC Solutions builds AI-driven search for e-commerce, enterprise, and support. From vector search to hybrid ranking and integration with existing content — we deliver search solutions that help users find what they are looking for.