Vector database

01why it matters for a business

Vendors sometimes present the vector database as the heart of an AI project. It is usually a supporting component. The decision that matters is whether your existing data platform can handle vector search at your scale, which it often can, versus adding a new system your team has to secure, back up, and pay for. Fewer moving parts generally means fewer failures and simpler compliance.

What matters more than the product choice is what goes into it: which content is indexed, how it is split, what metadata travels with each item (source, date, owner, access level), and how the index stays in sync when documents change or are deleted. Stale or orphaned vectors are a common source of wrong answers and, with sensitive data, of compliance trouble.

02what it looks like in practice

A mid-sized insurer already runs PostgreSQL for its policy administration system. For an internal assistant over underwriting guidelines, it adds the pgvector extension and stores embeddings alongside each guideline's metadata. Access rules come from existing tables, so an underwriter's search only returns guidelines for their lines of business. A nightly job re-embeds changed documents and deletes vectors for retired ones. No new database vendor was needed.

03common mistakes

• Buying a dedicated vector database before checking whether your current database can do the job.
• Storing vectors without metadata, which makes filtering by permission, date, or source impossible.
• No sync with the source of truth. Deleted documents must disappear from the index too.
• Choosing on benchmark speed when your data volume is modest and any option is fast enough.

04related terms

• EmbeddingsAn embedding is a list of numbers that represents the meaning of a piece of text, an image, or other data, produced by an embedding model.
• Retrieval-augmented generation (RAG)Retrieval-augmented generation (RAG) is a technique in which an AI system first searches your own documents or data for passages relevant to a question, then gives those passages to a language model to write its answer.
• Data warehouseA data warehouse is a central database designed for analysis and reporting, where data from many operational systems, such as ERP, CRM, billing, and marketing platforms, is collected, cleaned, and organized so it can be queried together.
• AI knowledge baseAn AI knowledge base is a company's documents, policies, procedures, and records organized so an AI assistant can search them and answer questions with citations.

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