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.