Start with your company website. Orivael reads your public home page and builds a first-pass map of where AI could work freely, where it should ask, and where it should stop.
A hypothesis built from what your public page shows. Confirm one workflow and Orivael can turn the map into enforceable boundaries.
An agent wants to issue a refund through the payment system.
Your rule requires a person to approve refunds above $100. The payment tool has not been called.
The goal is not to slow your agents down. It is to make their authority explicit, enforce it at runtime, and keep proof of what happened.
Define authority around actions that matter: how much it may spend, what it may change, which tools it can use, and when a person must step in.
Agents keep planning and requesting actions normally. Orivael evaluates the consequential step before the real tool is allowed to execute.
Each important decision can leave a signed record of the request, rule, decision and outcome so teams can reconstruct what the AI actually did.
Orivael gives autonomous systems something models alone cannot reliably give themselves: a boundary outside the model that decides what is actually allowed to happen.
For engineering teams that want to go deeper, Orivael's product family handles build-time workflow, runtime authority and operational evidence.
Checks consequential actions against explicit authority before execution.
Design, debug, replay and version agent workflows before they reach production.
Connect agent and model usage to cost, interventions and completed work.
Tamper-evident records for the decisions and actions that actually matter.
Start with your website for a first-pass map read from your public page. When it looks close, add one real workflow and Orivael can show where AI should be free, where it should ask, and where it should stop.