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The 80% problem

Frameworks like LangChain and LlamaIndex are great for prototypes. You can follow a tutorial, connect your documents, and have a working demo in a few days. Run it on a few documents and the results look promising. Then you deploy to production. Results appear to be subpar, users quickly notice. Getting from 80% to 95% takes months of work:
  • Building and testing document parsing pipelines
  • Experimenting with chunking strategies and chunk sizes
  • Tuning hybrid search (semantic + keyword)
  • Configuring and testing rerankers
Each component affects accuracy, and getting the compounding benefit of optimizing every step is a full-time job.

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What RAG-as-a-service provides

Instead of building and maintaining this infrastructure yourself, RAG-as-a-service gives you:
  • Parsing and chunking — Optimized pipelines that produce clean, logical chunks across 22+ file formats without custom code per format.
  • Query generation — Automatic multi-query expansion from conversation context, covering more ground than a single hybrid search.
  • Reranking — Built-in reranking that significantly improves chunk relevance, often compensating for suboptimal upstream choices.
  • Scale — Ingest and search millions of documents without managing vector storage, object storage, indexing, or compute.
  • Continuous improvement — Automatic access to new retrieval methods as they emerge, without changing your code.
Ready to give it a try? The quickstart walks you through your first search in under 5 minutes.