About the service
I create search that understands the meaning of a query, not just word matches. Over the years I've designed and launched semantic search for catalogs, knowledge bases, support services and internal document repositories. The work starts with your data and your users: what they type, what they actually want to find and where regular keyword search silently fails them. From there I select a suitable embeddings model, build a clean data loading pipeline and raise a vector database for your scale.
On the engineering side I deploy pgvector, Pinecone or Qdrant for your stack and budget, then design hybrid ranking: dense vectors are combined with classic key signals to get both completeness and accuracy. I tune relevance on real queries, add filtering, re-ranking and caching, and wrap it all in a clear API that your team calls with a single request. If you're building data retrieval for a chatbot or assistant, I assemble the RAG layer completely.
You get a measurable result: relevant results at the top, fewer empty answers and search that finally understands user intent.
On the engineering side I deploy pgvector, Pinecone or Qdrant for your stack and budget, then design hybrid ranking: dense vectors are combined with classic key signals to get both completeness and accuracy. I tune relevance on real queries, add filtering, re-ranking and caching, and wrap it all in a clear API that your team calls with a single request. If you're building data retrieval for a chatbot or assistant, I assemble the RAG layer completely.
You get a measurable result: relevant results at the top, fewer empty answers and search that finally understands user intent.
