Yes, and the design is the same whether the material is a manuscript under copyright, a client's site, or a household's own records.
Bulk reading goes to a self-hosted local model on my own hardware, a Mac Studio in this case. Chunking, scene maps, passage extraction and first-pass summaries all happen there, and the source text never leaves the machine. What travels to a frontier model, when judgement is worth paying for, is paraphrase: a scene map, a candidate learning, a question and the rows retrieved to answer it. The rule is written down and binding: learnings are paraphrase, evidence may quote a sentence or two with attribution, and unpublished manuscripts are never library sources.
The split is by kind of work rather than by size. Volume is local and cheap; judgement goes to the stronger model, or to me. That is what kept the cost flat as the libraries grew, and it is why the newest library can answer a question with citations while the Wi-Fi is off.
The one operational cost is discipline about the hardware. The daily model and the large ingestion model cannot both be resident on a 32 GB machine, so an ingestion checks every scheduled agent's calendar for a safe window, swaps the model in, proves it with a one-token probe, and always swaps back. An agent that wants to read a book must ask which library it belongs to first.
My own answers, from my own work. They change when the work does.