Caustic learns which region of your corpus a query belongs to and searches only that slice. On our first real-corpus pilot it removed ~75% of vector-search cost while keeping 95% of the documents a full scan would return — measured, not modeled.
Standard RAG embeds your query and scores it against the entire corpus, every time. But a billing question never needed the API reference; a code query never needed the legal docs. You pay to search all of it anyway. In retrieval-heavy and no-LLM pipelines that search is the dominant cost; behind an LLM it is a smaller slice, but one you pay on every single query.
A diagnostic and a lightweight router in front of your existing vector store. No re-embedding your corpus, no new model.
Before anything, Caustic reads your corpus geometry and tells you whether — and how — it separates into regions. If it can't be routed, it says so, instead of selling you savings that aren't there.
It discovers the corpus's natural regions automatically, or routes on a feature you already know (doc type, source, format). Organize once; the map is your reusable reference.
Each query is classified to its region and searched there. Uncertain queries route to their top few regions — never a full-corpus fallback — so ambiguity costs a little, not everything.
A savings number with no recall attached is meaningless — you can always "save" by searching less and finding less. So every figure here is paired with the recall it holds, measured against an exhaustive search on a real corpus. Pick the recall you need; read off the savings.
Search a quarter of the corpus, recover 95% of what a full scan returns. The everyday operating point.
Accept a little more miss rate and savings climb. You choose where on the curve to sit.
The best any router could do on this corpus knowing the answer in advance. Caustic lands ~4 points under it, and ~6 points over a standard fixed-budget baseline — for free.
Naive routing needs you to already know what separates your corpus — and dumps every uncertain query into a full search. Caustic diagnoses what separates your corpus (embedding structure, format, metadata — or tells you nothing does), and routes ambiguous queries to their top few regions, not the whole thing.
We ran Caustic end to end on a real corpus and measured the full recall-vs-savings curve — recall scored against an exhaustive search, every savings figure paired with the recall it holds. No cost-accounting model, no projected numbers.
Caustic sits in front of your vector store. Diagnose, organize once, then route. Your store does the similarity search; Caustic picks which slice it runs on.
If you run a RAG pipeline with a real corpus and a real embedding bill, we'll diagnose its routability, show you the savings-vs-recall curve on your data, and you keep whatever we save. No "buy now" — this is early, and we'd rather earn the number than quote one.
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