Request in → generated response out.
Control model / 01
The control layer around every AI request
An AI model generates outputs. It does not own tenant permissions, evidence standards, retention rules, provider policy or the decision to abstain. Mentaview owns that control path around the model.
Control path
It helps an AI product decide what to do before a model answers — and check what happened before the answer is trusted.
Request + permissions → planned path → allowed capabilities → checked answer or clear refusal.
01
Why another layer?
The difficult questions begin after you choose a model.
Teams still need to decide what data the system may see, when extra work is worth the cost, how evidence stays attached to claims and what happens when the system cannot support an answer.
A model is not a complete product
A model can generate text. It does not automatically know your permissions, evidence rules, budgets, retention policy or when it should refuse.
Not every request needs the same path
A simple question may need one direct answer. A difficult one may need private documents, fresh research, several checks or an honest 'not enough evidence'.
A confident answer can still be incomplete
Good wording does not prove that every part of a question was answered or that each statement has adequate support.
Changing providers should not change your rules
Your product policy should survive a change of model, cloud, database, device or deployment location.
02
What it does
A control layer for the whole answer path.
Mentaview sits between a product and the AI capabilities it may use. It does not force every request through every module.
- 01
Understand the request
Mentaview identifies what the user is asking and what a complete answer must cover.
- 02
Choose the smallest sufficient path
It decides whether direct model use is enough or whether memory, documents, research or another capability is justified.
- 03
Apply the rules
It checks which data, providers, tools and locations are allowed for this user and this task.
- 04
Check the result
It tests whether the answer covers the request and whether its important claims have enough support.
- 05
Return a clear outcome
It returns the answer and useful references, or says what is missing instead of hiding the gap.
Why “smallest sufficient path” matters: it can reduce avoidable calls and latency, but it is not a promise that every task will be cheap or fast. The path must still meet the task's evidence, privacy and quality needs.
03
Why it may matter
Different teams gain control over the same system.
The value is operational, not magical: clearer decisions, clearer boundaries and better evidence about what the product actually did.
Make behavior part of the product design.
Define when the system searches, remembers, escalates or refuses instead of inheriting those choices from one prompt.
Keep one contract across changing infrastructure.
Swap qualified models, stores and deployment profiles without moving business rules into every client.
See where data and decisions cross boundaries.
Make permissions, egress, retention, evidence and failure behavior explicit and testable.
Evaluate claims against visible limits.
Separate what is implemented, in controlled pilot, demonstrated by a canary or still on the roadmap.
04
Useful boundaries
Mentaview does not replace everything around AI.
It coordinates named responsibilities. It still depends on the quality of the chosen models, data, infrastructure and product decisions.
An AI model
It can use different model providers or local inference behind explicit adapters.
Your source of truth
It can retrieve and govern evidence, but your organization still owns its records and their quality.
A finished user interface
It is headless infrastructure designed to sit behind products, tools and devices.
A certification shortcut
It prepares evidence and controls. Independent assurance still requires scope, operation and qualified reviewers.
Request lifecycle
A private-document request exposes every control point.
Permissions, question obligations, evidence scope, model routing, answer checks and refusal conditions remain explicit from input to output.