We build retrieval systems that move beyond keyword matching — hybrid search, embeddings, ranking, and evaluation.
Most search problems aren’t really about choosing a vector database. They’re about understanding the queries users actually type, the documents you’re retrieving against, and what “good” looks like. We start there.
What we typically deliver
- Hybrid search systems combining lexical (BM25) and semantic (embedding) signals
- Vector database integration (pgvector, Pinecone, Weaviate, Qdrant) — we’ll tell you honestly when Postgres is enough
- Embedding model selection, fine-tuning where it earns its keep
- Reranking and result fusion
- Evaluation harnesses so you can measure quality and ship improvements with confidence
- RAG pipelines for LLM-grounded answers when that’s what you actually need
Who this is for
Teams running search inside a product (e-commerce, internal knowledge bases, support, vertical SaaS) where retrieval quality directly affects the user experience.