Retrieval-Augmented Generation that actually retrieves the right passage
Hybrid search plus reranking means the model sees the best evidence before it writes a single word.
RAG is only as good as its retrieval. NovaBOT combines dense vector search (semantic meaning), full-text keyword search (exact terms), and fuzzy trigram matching (typos and paraphrases), then fuses the results with reciprocal-rank fusion.
A reranking pass promotes the most relevant passages to the top before they enter the prompt — the single biggest lever on answer quality — so the model reasons over evidence, not noise.
The result: precise answers with citations, even across large, messy knowledge bases spanning websites, PDFs and support tickets.
Highlights
Hybrid retrieval
Vector + keyword + fuzzy search catches both meaning and exact terms.
Reranking
The most relevant passages are surfaced before the model writes.
Inline citations
Each fact links back to the exact source passage.
Frequently asked questions
What is RAG?
Retrieval-Augmented Generation retrieves the most relevant passages from your knowledge base and feeds them to the language model, so answers are grounded in your real content instead of the model’s memory.
Why is hybrid search better than vectors alone?
Pure vector search can miss exact terms like SKUs, error codes or product names. Combining it with keyword and fuzzy matching captures both semantic meaning and precise strings.
See it in action
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