A production-grade retrieval agent teams drop into their existing chatbot.
An embeddable RAG service with ingestion, retrieval, grounding and evaluation — designed to plug into any chatbot or product surface.
Teams shipping chatbots kept rebuilding the same retrieval stack — chunking, embeddings, re-ranking, grounding, evals — and kept getting the same production issues: hallucinated answers, stale sources, and no observability into why an answer went wrong.
A hosted RAG agent with a typed API and a drop-in JavaScript client. Ingest documents from URLs, files or connectors; ask questions from any chatbot with a single call; get grounded answers with cited sources, streamed to the UI.
- Ingestion workers with content-aware chunking and metadata extraction
- Vector store (pgvector) with hybrid retrieval and re-ranking
- Prompted answer synthesis with strict grounding and citation extraction
- Evaluation harness for regression tests on real question sets
- Per-tenant isolation, PII redaction and audit trail on every query
The system, module by module.
Ingestion
URL, file and connector ingestion with content-aware chunking and metadata.
Retrieval
Hybrid search (BM25 + vector) with a re-ranker to surface the best evidence.
Answer synthesis
Grounded generation with inline citations and streaming to the chatbot UI.
Evaluation
Golden sets, LLM-judge scoring and regression alerts on every deploy.
Customers ship a grounded assistant in an afternoon instead of a quarter, with evaluation in place from day one.
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