Building reliable AI experiences with clarity and care.

Embeddings transform raw text, images, or structured records into dense vector representations that capture semantic meaning. They power semantic search, recommendation engines, RAG pipelines, anomaly detection, and much more.

I work with state-of-the-art embedding models — from sentence-transformers and OpenAI embeddings to custom fine-tuned bi-encoders — and pair them with vector stores like Qdrant, Pinecone, and pgvector to build production-ready retrieval systems.

Key use-cases: semantic document search, knowledge-base Q&A, product recommendation, duplicate detection, cross-lingual retrieval.