Vector database implementation for search that actually understands the question.
Keyword search misses the point when someone phrases a question differently than your content does. Vector databases store meaning, not just text — powering semantic search, recommendations, and the retrieval half of RAG. We handle the embedding pipeline, database choice, and the architecture around it.
Vector search, applied.
Embedding pipeline design
Turning your documents, products, or records into searchable vectors, kept up to date as content changes.
Database selection & setup
Pinecone, Weaviate, pgvector, or another option — matched to your scale and existing stack.
RAG architecture
The retrieval layer that grounds AI answers in your real content instead of guessing.
Semantic & hybrid search
Meaning-based search, or a blend of keyword and semantic when that fits better.
Recommendation systems
Similarity search that powers "more like this" and personalization features.
Scaling & performance tuning
Indexing and query strategies that stay fast as your data grows.
Tools we pair it with
When a vector database is the right call — and when it isn’t.
A vector database earns its place when people search in their own words and your content uses different ones: help centers, product catalogs, internal documents, or any AI assistant that needs to pull the right passage before answering. It is unnecessary for a small, well-organized set of content, where ordinary search or a filtered database query is faster to build and easier to maintain. For many businesses, adding pgvector to a Postgres database you already run is enough. We would rather recommend the simpler setup and move up only if your data or traffic demands it.