Vector infrastructure that actually retrieves the right thing
We design, build, and tune vector database infrastructure for AI applications, from choosing the right store to the embedding pipeline, hybrid search architecture, and performance tuning that keeps retrieval fast and accurate under production load.
Vector stores built for production retrieval quality
A vector database that returns wrong results at low latency is not an improvement over no vector database.
Vector store selection & setup
Choosing the right vector database for your workload, Pinecone, Weaviate, Qdrant, pgvector, or Chroma, based on your data volume, query patterns, and infrastructure constraints.
Embedding pipeline design
Building the pipeline that converts your data into vectors, model selection, chunking strategy, batch processing, and the incremental update logic that keeps your index current.
Hybrid search architecture
Combining vector similarity search with keyword search and metadata filtering to get retrieval results that reflect both semantic meaning and structured business logic.
Vector DB performance tuning
Diagnosing and fixing slow query performance, index bloat, and recall degradation, the operational challenges that emerge once a vector store is running against real production traffic.
The memory layer of every serious AI application
Every serious AI product has one
Vector databases are the memory layer of modern AI applications. RAG systems, semantic search, recommendation engines, and agent memory all depend on a well-designed vector store. Getting it wrong early is expensive to fix.
The choice matters more than people think
Pinecone, pgvector, Weaviate, and Qdrant make different tradeoffs around latency, cost, filtering capability, and operational overhead. The right choice depends on your specific workload, not a generic benchmark.
Retrieval quality is measurable
We instrument every vector store we build with recall metrics, latency tracking, and relevance scoring, so you can see whether retrieval is actually working, not just assume it is.
Need a vector store that actually returns the right results?
Tell us about your data and your retrieval requirements. We will recommend the right store and design the pipeline around it.
