The model question feels concrete, so teams start there. For sensitive research, model selection sits downstream of a bigger design choice: where the knowledge may exist, and how a person verifies what the system does with it.
Start with the boundary
A useful boundary states which sources are authorized, which are excluded, who may access them, what can leave the environment, and how long artifacts should exist. Private describes a set of operating decisions, and no product setting supplies them for you.
This work also reveals when local AI is unnecessary. If the source material is public, the workflow is low consequence, and policy permits cloud tools, a private system may add cost without enough value.
Define the human decision
Research workflows rarely end at an answer. A person decides whether to search again, challenge a source, change a method, include a claim, or take an action. Design the system around that decision and the evidence the reviewer needs.
- What decision follows the output?
- Who is accountable for reviewing it?
- What source evidence must be visible?
- What failure would be unacceptable?
Then choose the engine
Once the boundary, evidence, and decision are clear, model requirements become easier to evaluate. Context capacity, tool use, speed, memory demand, and reasoning quality can be compared against the actual workflow.
R.A.I.S.E. is model-agnostic by design for this reason. The durable asset is the controlled evidence system and evaluation process. Models can improve without forcing the organization to rebuild its institutional memory around a new vendor.
A pilot should produce a decision
A credible pilot ends with more than a polished demonstration. It should show where retrieval worked, where it missed, what reviewers trusted, what controls are still absent, and whether the workflow creates enough value to justify scaling.