AI governance is easy to talk about in principle. Putting it into practice is another matter.

As organisations move beyond experimenting with AI and start building agents into everyday business processes, new questions arise. Who decides what an AI agent is allowed to do? How are organisational policies enforced? And how do you maintain accountability without slowing everything down?

These are the challenges Caitlyn is designed to help address.

From AI policy to practical controls

Having an AI policy is a starting point. But what happens when someone in your organisation wants to build an AI assistant that interacts with customers or supports business decisions?

In this walkthrough, our CTO Sam Sehnert uses a fictional insurance company, Apex Insurance, to demonstrate how Caitlyn helps organisations manage that process.

The scenario follows an employee developing an AI assistant to answer customer insurance questions.

Rather than leaving governance to a separate review process, Caitlyn brings organisational rules, approvals and accountability into the workflow itself.

Watch the walkthrough below to see how it works.

What you’ll see in the demo

1. Human accountability

AI can help assess an initiative against organisational rules and prepare information for review. But a named person remains responsible for the decision.

2. Turning policy into enforceable rules

An organisation might have a policy that AI cannot make insurance claims decisions. Caitlyn demonstrates how those requirements can be incorporated into the governance process, with changes and responsibilities recorded.

3. Governance without unnecessary delays

The walkthrough also shows how teams can move from an idea towards a working pilot while following the organisation’s rules and approval processes.

Moving AI into production with confidence

The challenge for organisations isn’t simply adopting AI. It’s making sure the systems they deploy operate within clear boundaries, with appropriate oversight and accountability.

Caitlyn helps organisations build and operate trusted AI agents and workflows, with the controls needed to move beyond experimentation.