Custom AI Assistants for Research and Legal Teams: FAQ

Research and legal teams sit on mountains of PDFs, contracts, and prior work. A custom AI assistant can turn that into an on-demand specialist — but only if the fundamentals are right. These are the questions we hear most.

Can a custom AI assistant actually read our PDFs and internal notes?

Yes. A well-built assistant ingests PDFs, scanned documents, screenshots, meeting notes, and voice transcripts, then indexes them into a queryable memory. You chat with it the way you'd ask a colleague who has read everything.

Do we have to write prompts or code?

No. The point of a no-code assistant is that you connect your sources, and the system figures out how to reason across them. You ask questions in plain language.

How does this fit a clinical research workflow?

Typical uses include summarizing protocols, comparing prior study results, extracting inclusion criteria from a stack of papers, and flagging contradictions between sources. The assistant cites which document each answer came from.

How does this fit legal document review?

Common patterns: comparing contract versions, extracting clauses across a portfolio, checking a new agreement against a preferred playbook, and answering "has anything like this come up before" across historical matters — with citations back to the source document.

Where does our data live?

Sensitive workflows need self-hosted deployment on your infrastructure, running the model you choose. That way the sources, the memory graph, and the answers never leave your environment.

What happens if we switch models later?

The memory should belong to you, not the model vendor. If you own the memory, you can swap engines without rebuilding your knowledge base.

How do we get started?

Most teams begin by pointing an assistant at a single project's sources — one study, one matter, one deal — and expand once the workflow proves out. Explore SNAP AI to see how.