Service

Retrieval pipelines that find the right chunks

Closest-in-vector-space is not always the best answer. We build RAG systems with hybrid retrieval, reranking and access models that fit multi-user products.

Problems we solve

Vector-only retrieval misses exact terms

Acronyms and notation in research papers need BM25 alongside embeddings — then fusion and reranking for precision.

Shared documents without duplicate embeddings

Multi-user products should share one embedding set and revoke access safely — not re-embed every upload.

Vague conversational follow-ups

Query rewriting turns “why is it useful?” into a standalone retrieval query without losing user intent.

Capabilities

  • Document ingestion and chunking
  • Vector search with Qdrant
  • Hybrid BM25 + vector retrieval
  • Cross-encoder reranking
  • Shared multi-user vector access
  • Cited, streaming answers

Technologies

Tools used when they fit the problem — not the other way around.

QdrantFastEmbedBM25Reciprocal Rank FusionCross-encodersLlamaIndexLangChainPyMuPDFPostgreSQL

Related work