Solution · RAG
Ask a question. Get a cited answer.
Retrieve-then-generate over your own indexed content: a synthesized, cited answer, not a ranked list you sift through yourself. Streamed, with citations back to the source.
The alternative
What you'd otherwise build.
Most places hand you the pieces of RAG and leave the assembly to you.
- Extract and chunk every file type yourself. PDFs, images, spreadsheets, call transcripts: each format needs its own extraction step, then splitting into chunks without cutting a sentence at the worst possible spot.
- Stand up both a keyword engine and a vector index, then route between them. Full-text and semantic search solve different problems, and neither one alone is enough. Ship just a vector index, which is where most hand-rolled RAG stops, and exact-match queries degrade.
- Keep embeddings in sync as your corpus and your model change. A sync job to catch every new or edited document, and a re-embedding pass whenever you upgrade the model, or the index quietly drifts from what is actually true.
- Extract and attribute citations yourself. Mapping a generated sentence back to the passage it came from is its own, non-trivial problem.
- Enforce the same access control across every one of those systems. Extraction, the vector index, the keyword engine, and the model call each need to respect the exact same per-tenant isolation, or a generated answer can surface content the caller was never allowed to see.
How it's built
One endpoint, not a pipeline you own.
This is a solution, not a new building block: it's built directly on top of three building blocks already on this site. Nothing about your data model or access policy changes to add it.
Ingest
Extracted, chunked, and embedded into whichever schema you declare, indexed the moment it lands.
Search
The same index, full-text and semantic both, fused into one ranking to retrieve the passages worth answering from.
Inference
In-perimeter generation, grounded in what search found.
See ingestion, search, and inference for the mechanics underneath this page.
See it in a real app
Ask a question over real case notes.
The RAVV reference app asks questions over case notes today, for its admin role (a narrower role there is still waiting on a platform-side scope fix). Second Brain's pitch is the same idea at its widest: dump every note, idea, and link, then just ask it.
Read: the RAVV stack →Want the raw ranked list instead of a synthesized answer? See search. Already looking at one specific document and just want to ask about it, no retrieval step needed? There's a narrower call for that too.
See how it all fits together on the platform page.