The real test of a bibliographic assistant isn't the demo on ten articles: it's what happens when your corpus becomes massive. The more documents there are, the easier it is to surface a passage that looks like the answer without being the right one. Accuracy at scale is an engineering problem — and it's the one RefChat is built for.
Why volume breaks naive tools
Going from dozens to thousands of articles degrades two things. First, noise: dozens of neighbouring passages compete for relevance, and a single-stage engine lets too many through. Second, dispersion: the right information is often in a single paragraph of a single article, buried among thousands of others. A basic "RAG" that just does one semantic search loses accuracy at exactly the moment the user needs it most.
RefChat's approach: filter in several passes
Rather than a shortcut, RefChat chains complementary stages that reinforce each other as the corpus grows:
| Stage | What it brings at scale |
|---|---|
| Broad search | A first selection combines understanding of meaning and matching of exact terms, so nothing relevant is missed across the whole corpus. |
| Fine reranking | A second pass reorders the candidates and discards the "false friends" — passages that look like the answer without containing it. This is what keeps accuracy high when volume explodes. |
| Citation to the source | Only the best passages are presented, with their exact origin. You verify in one click, even on a corpus of several thousand articles. |
| Owning the refusal | If nothing supports an answer, RefChat says so. At scale, this guardrail is what stops noise from turning into false certainty. |
Multilingual, because science is
On a large corpus, your documents are rarely all in the same language. RefChat reasons about meaning: a question in English finds the answer in an article in French, German or Spanish, without you having to translate anything. The larger and more international the corpus, the more this matters.
Synthesise, not just retrieve
When a question spans dozens of articles at once — a state of the art, a literature review — RefChat aggregates information across documents and returns a sourced synthesis, where every element stays tied to its origin. You keep the overview and the traceability.
The bottom line
RefChat's strength doesn't show on ten PDFs: it shows on thousands. Multi-stage search that filters noise, reranking that removes false positives, native multilingual support, verifiable citations and an owned refusal: all mechanisms designed so that accuracy holds as the corpus grows. Where other tools dilute, RefChat stays usable.