Expert vector database implementation for AI applications. We design and deploy vector search infrastructure using Pinecone, Weaviate, pgvector, and Qdrant.
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Vector databases are the backbone of modern AI applications β powering RAG systems, semantic search, recommendations, and anomaly detection. We design vector infrastructure that balances accuracy, latency, and cost while handling the unique challenges of high-dimensional data at scale.
We work with all major vector databases β Pinecone for managed simplicity, Weaviate for hybrid search, pgvector for PostgreSQL-native workloads, and Qdrant for self-hosted control. Our embedding pipelines use OpenAI, Cohere, or open-source models depending on accuracy and cost requirements.
Teams building AI applications that require semantic understanding β RAG chatbots, search engines, recommendation systems, content discovery, and similarity matching. Whether you're choosing your first vector DB or scaling an existing deployment, we provide the expertise to get it right.
Analyze data types, query patterns, scale requirements, and latency constraints to select optimal vector DB.
Design indexing strategy, embedding pipeline, search architecture, and integration points with your application.
Deploy vector database, build embedding pipelines, implement search API, and integrate with application layer.
Tune index parameters, optimize chunk sizes, implement re-ranking, and benchmark query performance.
Deploy to production, set up monitoring dashboards, implement incremental updates, and establish SLAs.
Let's build vector infrastructure that powers accurate, fast AI retrieval for your application.
We implement and optimize Pinecone, Weaviate, Qdrant, Milvus, Chroma, and pgvector. We help you choose based on your scale requirements, query patterns, filtering needs, and whether you need managed or self-hosted solutions.
Vector database implementation at MicrocosmWorks ranges from $25-$50/hour, covering database selection, schema design, embedding pipeline development, indexing optimization, and integration with your AI application.
Yes, we optimize vector search using HNSW index tuning, quantization techniques, metadata filtering strategies, and sharding configurations to maintain sub-100ms query times even with tens of millions of high-dimensional embeddings.
We build automated embedding pipelines using change data capture or scheduled jobs that detect source data changes, regenerate embeddings, and update the vector database incrementally, ensuring search results always reflect the latest content.
We evaluate and benchmark OpenAI text-embedding-3, Cohere Embed, BGE, and open-source models like E5 and GTE based on your domain, language requirements, and cost constraints. We often fine-tune embeddings on your data for better relevance.