Vector databases specialists for your dedicated team.

Where Vector databases fits in a long-term product team.

The first question is usually whether a dedicated vector database is needed at all. Postgres with pgvector handles millions of vectors comfortably, and keeping vectors beside the relational data removes a system, a synchronisation path and a consistency problem. Dedicated databases earn their place at larger scale or under specific latency requirements.

Metadata filtering is where implementations differ most and where requirements are usually discovered late. Restricting a similarity search to one tenant, one date range or one document type is a common need, and systems handle it with very different efficiency: pre-filtering, post-filtering and hybrid approaches all perform very differently.

What your dedicated team does with Vector databases.

Pure vector search is frequently worse than a hybrid of vector and keyword search. Exact terms such as product codes, names and identifiers are what embeddings handle worst, and they are often what users search for.

Combining both is usually the biggest single improvement available to a retrieval system, the kind of judgement your dedicated AI team brings.

Common Vector databases use cases.

• Starting in the database you already run pgvector where volumes allow, avoiding a second system and its sync path.
• Filtered similarity search Tenant and date restrictions applied efficiently, inside the search itself.
• Hybrid keyword and vector retrieval Exact terms handled by keyword search, which embeddings are poor at.

Adding Vector databases skills to your team.

Retrieval infrastructure sits with the AI engineers we assign to you, starting from whether another system is needed at all.

You can add Vector databases capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.