Use case · RAG & Search

Ground answers in your own data

Assemble a complete retrieval stack — embeddings, reranking, and generation — on open models behind one API, with your corpus stored next to the GPUs.

The stack

Everything retrieval needs

Each stage uses an open model — no separate vendors to stitch together.

Embeddings

Vectorize documents and queries with open embedding models.

Reranking

Re-score retrieved candidates with a cross-encoder reranker for higher precision.

Grounded generation

Feed retrieved context into open LLMs for on-topic, source-grounded answers.

Document store on shared storage

Keep your corpus on a shared filesystem mounted right next to the GPUs.

One API key

Embeddings, reranking, and generation behind a single OpenAI-compatible API.

Multi-region

Low-latency retrieval and generation with automatic failover.

Build retrieval-augmented apps

Embed, retrieve, rerank, and generate — all in one place.