Retrieval that actually works.
Hybrid BM25 + vector search with p99 latency. Index, chunk, and retrieve your documents at scale — no infrastructure to manage.
What is RAG?
Retrieval-augmented generation grounds model responses in your own documents. Backboard indexes, chunks, and retrieves the most relevant context at query time — so answers stay accurate and current without retraining.
Hybrid search that finds the right context, fast.
p99 retrieval
Hybrid BM25 + vector search returns the most relevant chunks with low p99 latency, even across millions of documents.
Better recall
Reciprocal rank fusion blends keyword precision and semantic meaning to surface more relevant passages than either approach alone.
Infra to manage
Index, chunk, embed, and retrieve through a single API — no vector database or retrieval pipeline to build or maintain.
/01
Indexing
Ingest documents in any format and automatically chunk and embed them for retrieval — no manual preprocessing or pipeline to build.
/02
Hybrid Search
Combine BM25 keyword matching with vector similarity so retrieval works whether queries use exact terms or natural language.
/03
Reranking
Rank results by relevance with reciprocal rank fusion so the most useful passages reach the model first.
/04
Grounded Generation
Inject retrieved context into the model call so responses stay grounded in your actual documents — accurate, current, and citable.
Index your documents, retrieve what matters, and give every model call the context it needs.