RAG and AI agent platforms

Retrieval systems that answer from your documents, built to run multi-tenant in production.

Most retrieval prototypes answer well on a demo set and fall apart on real documents and real users. We build the parts that decide whether it survives contact with production: the ingestion pipeline, the retrieval strategy, the reranking, the caching, and the evaluation you need to know whether a change made it better or worse.

What you get

  • Document ingestion pipelines with chunking and embedding
  • Hybrid BM25 and semantic retrieval
  • Reranking with RRF and cross-encoder models
  • Query planning and expansion from conversation history
  • Semantic caching to cut redundant LLM calls
  • Streaming responses over Server-Sent Events
  • Per-tenant index isolation

Related work

Tell us what you are trying to build

Describe the system and the constraint you have hit. You will get a technical reply, not a sales sequence.