As AI search, AI summarization, and AI agents become part of everyday life, the standards for search are fundamentally changing. Search is no longer simply a technique for finding the same words. It has evolved into a process of understanding the intent and meaning of a user's query and finding the closest answer. The technology at the heart of this shift is the vector database (VDB). The reason AI search differs from traditional search can be summarized in one sentence: AI reads sentences, while VDB searches for meaning.

The evolution of search technology: From keywords to meaning

Traditional search engines operate based on keyword indices. The key focus is on whether a specific word appears in a document and how frequently it appears. However, this approach reveals its limitations in AI search environments. "When can I get a refund?" and "When will my cancellation be processed?" use different words, but their meanings are nearly identical. Keyword-based search views these as separate questions, but AI recognizes them as having the same intent. What's needed here is a structure that quantifies and stores meaning—a vector database.

What is Vector DB?

A vector database stores data like text, images, and audio in vector (arrays of numbers) format, calculating the distance between these vectors to find the most semantically similar data. AI models convert sentences or content into embeddings. These embeddings are stored in the vector database, and user queries are also converted into vectors to retrieve the most semantically relevant data. In other words, a vector database is a memory device that tells AI not "where something is," but "what is similar to what."

Why Vector Databases Are Essential for AI Search

AI search isn't just about simple querying; it's the starting point for inference. For AI to generate answers, it must first accurately identify a reliable collection of information. A vector database plays the following roles: First, it connects questions with similar meaning, even if they're worded differently. Second, it makes unstructured documents (FAQs, policies, reports) searchable. Third, it enables AI to directly cite up-to-date and internal data. Without this structure, AI can generate "plausible answers," but it struggles to produce accurate, sourced answers.

Critical differences between vector DB and existing DB

Traditional databases excel at precise condition matching. They are efficient when conditions are clear, such as IDs, dates, and status values. In contrast, vector databases excel at ambiguous queries. Questions with unclear conditions, such as "similar cases to this service," "related content to this document," or "customer complaints," are difficult to handle without a vector database. This is why AI search is replacing traditional search.

Utilization Scenarios from a Business Perspective

As corporate websites, internal documents, and customer inquiry data accumulate, the value of the vector database increases.

• Vectorize FAQs and policy documents for AI-powered customer service.
• Integrated search of multilingual content based on meaning
• Link IR, ESG, and technical documentation to AI summaries and search.

It's important to note that a vector database doesn't operate in isolation. It must be designed in conjunction with a CMS, log data, permission management, and up-to-dateness management to create a truly operational AI search system.

Common Misconceptions When Introducing Vector DB

The most common misconception is that “AI search is possible with just a vector database.” In reality,

• Which data to vectorize?
• How often will you update?
• Without first defining what data AI can cite, vector databases remain mere experimental tools. They are part of a search strategy, not a technology.

Commonalities seen in reference cases

Organizations where AI search works well commonly treat their vector databases as "digital infrastructure" rather than "repositories." Data is vectorized as soon as it accumulates, and the structure is designed to ensure that AI always references only the most up-to-date and reliable information. This difference determines the quality of AI search.

Insight Summary

Vector databases are the hidden key that make AI search possible. While AI speaks well because of models, vector databases provide accurate answers. In the era of AI search, the crucial question is not "Which AI model should we use?" but "What will AI base its answers on?" The answer to that question lies in vector databases.