Semantic search for SaaS: When keywords aren't enough

Semantic search is an advanced technique that enhances search functionality by understanding the intent and context behind queries rather than relying solely on exact keyword matches. Utilizing vector embeddings and transformer neural networks, semantic search captures the meaning of queries, enabling systems to retrieve relevant results even when the exact terms are not present. This approach is particularly beneficial in SaaS applications where users often express natural language questions or face diverse vocabularies, reducing zero-results rates and improving user satisfaction. Implementing semantic search involves generating vector representations of text, storing them in specialized indexes, and using technologies like Redis for efficient processing. Key applications include enterprise knowledge management, customer support, developer tools, and e-commerce product discovery. Semantic search requires more sophisticated infrastructure compared to keyword search, demanding decisions about embedding models, vector storage, and caching strategies. A hybrid search approach, combining keyword precision with semantic contextual understanding, is often recommended for SaaS platforms to validate improvements incrementally and cost-effectively.