Clear data boundaries before confident automation.
Review how InsightWonder approaches AI execution, project-level data boundaries, integrations, evidence and policy.
Keep every AI action explicit and reviewable.
See what runs, when it runs and which data becomes part of a formal project.
Find the policies and documentation your review needs.
Questions teams ask before they start
Does a website visit trigger model processing?
No. Marketing pages do not call AI models. A costly task starts only after a user explicitly confirms a Tools or App action.
Is snapshot data automatically a formal project baseline?
No. Snapshot results remain limited and unconfirmed until the user claims them and completes onboarding.
How are detailed security questions answered?
Detailed responses are based on verified technical controls, data-flow documentation, subprocessors and approved legal policies.
Where is data stored?
Business data is stored in Postgres with tenant isolation enforced at the query layer. Raw collected evidence such as HTML and screenshots is kept in object storage.
How are third-party tokens handled?
Tokens for connected channels and model APIs are encrypted per tenant. They are never written into code or logs.
Can a connected channel post without approval?
No. External publishing is draft-first by design and requires an explicit human confirmation before anything leaves the workspace.
Turn AI visibility into a repeatable growth system.
Create a project, confirm your market and start measuring the questions that matter.