Embedding strategy
Choose embedding models, dimensions, chunking and update strategies around your actual data.
EMBEDDINGS & VECTOR SEARCH
Search by meaning,
not just keywords.
I build embedding and vector-search systems for semantic retrieval, recommendations, clustering, deduplication and RAG applications.
Build semantic search →What I can build
Vector databases
Implement pgvector, Pinecone, Weaviate, Qdrant or other vector storage where appropriate.
Semantic search
Build search that understands similarity and intent beyond exact keyword matching.
Hybrid search
Combine vector similarity with lexical search, filters and business rules.
Similarity & matching
Use embeddings for recommendations, entity matching, deduplication and clustering.
Evaluation & tuning
Benchmark retrieval quality and tune thresholds, chunking, models and ranking.
Bring the problem, data or current prototype.
I can help scope the right architecture and turn it into a production-ready implementation.
Discuss your project →