What is Semantic Search?

What Is Semantic Search?

Semantic search is a search technology that understands what you mean, not just what you type. Traditional keyword search works by looking for exact word matches -- if you search for "budget laptop," it finds pages containing those specific words. Semantic search goes further by understanding the intent and meaning behind your query, returning results that are conceptually relevant even if they use different terminology.

For example, if an employee searches your company knowledge base for "how to handle an unhappy customer," semantic search can find documents titled "Customer Complaint Resolution Process" or "De-escalation Techniques for Client Relations" -- even though none of those titles contain the words "unhappy" or "handle." This is because the search system understands that these concepts are related in meaning.

How Semantic Search Works

Semantic search relies on embeddings -- numerical representations of text that capture meaning. The process works as follows:

1. Indexing phase: All searchable content (documents, product listings, support articles, etc.) is converted into embeddings using an AI model and stored in a vector database
2. Query phase: When a user enters a search query, that query is also converted into an embedding
3. Matching phase: The system finds stored embeddings that are closest in meaning to the query embedding
4. Ranking phase: Results are ranked by semantic similarity and returned to the user

Many modern implementations combine semantic search with traditional keyword search in a hybrid approach, using the strengths of both methods. Keywords handle precise matches (product codes, names, technical terms) while semantic understanding handles conceptual queries.

Business Impact

Dramatically Better Search Quality Studies consistently show that keyword-only search fails to return relevant results 20-40 percent of the time, primarily because users and content authors use different words for the same concepts. Semantic search closes this vocabulary gap, improving the percentage of successful searches significantly.

Customer-Facing Applications

• E-commerce product discovery: Customers can search naturally ("something warm for rainy season") and find relevant products without needing to know exact category names or product terminology
• Help centers and FAQ pages: Customers describe their problems in their own words and receive relevant solutions, reducing the load on human support teams
• Content platforms: Users find relevant articles, videos, or resources based on topics and themes rather than exact title matches

Internal Applications

• Knowledge management: Employees find company policies, procedures, and past project documentation faster, reducing time wasted searching for information
• HR and compliance: Staff can search policy documents using natural language questions rather than trying to guess document titles or section headings
• Sales enablement: Sales teams quickly find relevant case studies, proposals, and competitive analysis based on deal context rather than file names

Implementing Semantic Search

A practical semantic search implementation involves several components:

• An embedding model to convert text into vectors (options range from OpenAI's API to open-source models you can host yourself)
• A vector database to store and search the embeddings (Pinecone, Weaviate, Qdrant, or pgvector)
• A search interface that sends user queries through the embedding model and retrieves results from the vector database
• Optional: a reranking model that further refines result quality by re-scoring the top results

Relevance for Southeast Asian Businesses

Semantic search offers particular advantages in ASEAN markets due to the multilingual nature of business in the region. A well-implemented semantic search system can match queries in one language to documents written in another, enabling companies to serve customers and employees across language boundaries without maintaining separate search indexes for each language.

For e-commerce businesses across Indonesia, Thailand, and the Philippines, semantic search can dramatically improve product discovery rates, directly impacting conversion and revenue. Customers who find what they are looking for buy more and return more often.

Implementation costs are accessible for SMBs. A basic semantic search system can be built and deployed for USD 200-500 per month in infrastructure costs, with the most significant investment being the time to set up the embedding pipeline and integrate it with your existing search interface.