Your policy. Applied correctly.

When AI gets it wrong, you answer for it.

A policy implemented incorrectly is policy not implemented.

Sarah leads AI for a company building AI products.

She could be in finance, healthcare, insurance or any other high stakes industry.

If a chatbot says something it should not, a business critical decision is made wrongly or a patient gets a recommendation the system should never have made, Sarah is the one who has to answer for it.

Trust is damaged, revenue is lost, regulators ask questions and engineering is blamed.

What teams often do today

Teams take approved policy and implement it into the system.

Approved policy is translated into prompts, code, retrieval and workflow logic.

The policy side cannot clearly verify what was actually implemented.

Live system behaviour sits with engineering, not with the people who wrote the policy.

If something fails that should have been prevented, engineering carries the responsibility for what is live.

What ARCS is

ARCS is an AI governance implementation platform for helping organisations make sure approved policy is applied correctly in live AI systems.

ARCS is designed to work with existing AI applications and tech stacks.

It acts as a visible control layer between approved policy and live AI behaviour.

It helps teams see:

what policy is running
where policy runs
when rules activate
how policy is applied
what result should follow

How ARCS integrates with your AI application

ARCS connects policy to live decision points in your existing AI application.

It is designed to work with your current system and infrastructure, so your team keeps control of the application, environment and decision flow.

Teams can use the ARCS Control Panel to write, edit, update, compile, activate and inspect rules and to inspect runtime behaviour.

Rules are compiled into canonical JSON for runtime use.

It gives both policy and engineering teams a shared place to review what policy is active and how it is behaving in the live system.

A Visual Studio Code extension is also provided for creating rules and building the domain vocabulary behind them.

Version 1 uses a Python runtime, but it can be called from any language.

Without ARCS. With ARCS

Without ARCS

Governance and policy teams approve policy, then it is handed to engineering for implementation.

It gets translated into prompts, code, tools, and workflows across the live system.

Once live, the people who wrote the policy cannot clearly see what is running or whether it is being applied correctly.

If something goes wrong, engineering carries the responsibility for what is live.

With ARCS

Once policy is implemented, governance and policy teams can still review it in the live system.

They can see in simple English what policy is active, where it runs, when it activates, how it is applied, and what result should follow.

When policy changes or needs correcting, updates can be made faster and verified more clearly in production.

That makes it easier for both policy and engineering teams to carry responsibility for making sure policy is applied correctly.

ARCS helps ensure approved policy is applied correctly in the live system, where AI decisions are actually made.

Policy more people can understand

ARCS defines policy in one place, in an English like language that policy, governance and technical teams can work with. That makes it easier to review, update and keep aligned with what has been approved.

Choose where policy is checked

ARCS gives you 26 execution boundaries across the AI stack, including model calls, tool calls, retrieval, memory and workflow transitions. Checks can be configured before or after those steps, so policy runs where behaviour actually needs to be controlled.

Choose when a rule should apply

Not every rule should run all the time. ARCS gives you 10 built in ways to decide when a rule applies, for example by user, input, tool, workflow step or risk condition.

Choose how policy is enforced

Different situations need different enforcement methods. ARCS gives you 7 practical ways to enforce policy, from direct checks at the point of action to framework and network level control.