Stop demoing “smart chat”.
Ship assistants grounded in your actual data.
Index your dataset in minutes, not weeks.
Rerank to keep relevance high as data grows.
Citations + traces so teams can trust outputs.
Designed for grounded RAG workflows
Retrieval, rerank, and generation — without platform chaos.
Dataset-scoped context
RAG stays tied to your dataset versions, not random prompts and fragile notebooks.
Audit-friendly runs
Trace inputs/outputs with reproducible context so teams can debug and govern.
Production behavior
Same contracts you test in Playground are the ones you ship to production endpoints.
RAG Runtime (Beta)
Gateway-based RAG: your LLM runs outside. Xalorra keeps dataset scope, versions, and traceable runs coherent.
Embeddings index
Retrieve
Rerank
Citations
Traces
Build the assistant
Teams that build RAG
Pick a dataset + namespace and generate a domain assistant from grounded context.
Chunking + embeddings are versioned so runs stay reproducible.
Swap LLM providers without rewriting workflows.
Prefer to explore first? Open Playground.
Improve relevance
Teams that rerank retrieval
Rerank improves precision when top-k retrieval gets noisy.
Keep citations consistent even as the dataset grows.
Measure quality by looking at traces and retrieved chunks.
Prefer to explore first? Open Playground.
Govern what you ship
Teams that govern runs
Audit inputs/outputs and track dataset versions across runs.
Tenant-aware isolation to prevent cross-org leakage.
Operational clarity: what changed, when, and why.
Prefer to explore first? Open Playground.
RAG you can audit later
Grounded answers are a workflow, not a prompt.
If you care about tenant isolation, versioned datasets, and traceable runs, you want a playground that behaves like production.