Extraction, embedding, hybrid search, reranking, and cited answers: one managed layer. You choose the models and tune every stage. You don’t host or scale any of it.
Not one model doing all the work. Every stage below runs on named infrastructure. Some of it yours to choose, all of it managed for you.
Each page is read to text, figures and tables included, so everything on it can be searched.
Documents are split along their own structure, boilerplate dropped and tables kept whole.
Each chunk becomes a vector that captures its meaning, using the model you choose.
Semantic and keyword search run together for a wider candidate pool, or either can run alone.
A cross-encoder rereads every candidate against your question and reorders them, the single biggest lever on quality.
The answer is generated only from the retrieved passages, each citation pointing to its exact source region.
Fourteen enterprise documents, indexed and searchable. Every score, rank, and millisecond below is what the system returned for your query.
Pass-through, per-unit metering across every stage. No minimums, no seats. Pick a model per stage and pay for exactly what you index and query.
Write the cited answer with any language model, from fast and cheap to frontier. Browse every model on Remy →