Your AI product. Governed correctly.

A dedicated governance control layer for production AI teams.

Do what you do best

Building the AI product is already complex.

Teams building production AI already have to manage models, prompts, tools, retrieval, workflows, permissions and user experience.

Governance adds another hard problem.

What policy should apply?
Where should it be checked?
Who should review higher risk decisions?
What evidence should be captured when the system allows, blocks or escalates a decision?

Product teams should be able to keep building the AI application.

Policy, risk and compliance teams should be able to bring their expertise into how live AI decisions are controlled.

ARCS Control Hub gives teams a dedicated governance control layer, so each team can do what they do best while everyone can see what governance is active and how decisions are controlled.

What teams often do today

Today, approved policy is often translated into prompts, code, retrieval rules and workflow checks.

Once it is inside the application, the people who understand the policy can struggle to see exactly what is running.

Product and engineering teams carry the implementation burden, while governance, risk and compliance teams lose a clear view of how live decisions are being controlled.

The problem is the lack of a shared governance layer where each team can do what it does best.

A dedicated governance control layer

ARCS Control Hub is AI governance control software for teams building production AI.

It works with existing AI applications and gives policy, product, engineering, risk and compliance teams one shared place to see how live AI decisions are controlled.

Teams can define what policy should apply, where checks should run, when a decision needs review and what evidence should be captured.

ARCS helps teams see:

• what governance is active
• where policy is checked
• when rules apply
• why a decision was allowed, blocked or sent for review
• what evidence was captured

How ARCS integrates with your AI application

Your application calls ARCS from your backend code when a governance check is needed.

ARCS can be called before or after key AI moments, such as:

• before a model response is returned
• after a model response is generated, before it is shown to the user
• before retrieved content is trusted or used
• after retrieval, to record what context was used
• before a tool is called
• after a tool returns a result

Each check tells ARCS where it is happening using three fields:

• boundary: the type of AI moment being checked, such as model call, tool call, retrieval or workflow
• target: the specific action, tool, model, workflow or decision being governed
• stage: when the check happens, such as before, after or during the action

This gives ARCS a structured map of where governance runs across the live system.

It also helps Control Hub show useful views, such as which parts of the application are governed, where rules are active, where reviews happen and where evidence is being captured.

Example JavaScript call:

const decision = await arcs.evaluate({
  boundary: "tool.call",
  target: "loan.approve",
  stage: "pre",
  context: {
    customerType: "consumer",
    amount: 12000,
    riskLevel: "high",
    aiConfidence: 0.71
  }
});

if (decision.outcome === "allow") {
  return approveLoan();
}

if (decision.outcome === "require_review") {
  return sendToHumanReview(decision.evidence);
}

if (decision.outcome === "block") {
  return blockDecision(decision.evidence);
}

ARCS can return an outcome, matched rules, review requirements, obligations, reasons and decision evidence.

Your application keeps control of the user experience.

ARCS provides the governance control layer around the decision.

ARCS Control Hub is being built as a multi tenant SaaS application, with local deployment also planned for organisations that need governance controls to run inside their own environment.

Without ARCS. With ARCS

Without ARCS

Product and engineering teams have to build governance logic into the AI application themselves.

That means turning policy requirements into prompts, code, tool checks, retrieval rules, workflow logic, review paths and evidence capture.

They can do it, but it becomes another system to design, maintain and explain alongside the actual product.

Governance, risk and compliance teams still own the policy, but they can lose a clear view of how that policy is running in the live system.

With ARCS

ARCS gives governance its own dedicated control layer.

Product and engineering teams can focus on the AI application, user experience and decision flow.

Policy, risk and compliance teams can bring their expertise into how live AI decisions are controlled.

Everyone works from one shared view of what governance is active, where policy is checked, when rules apply and what evidence is captured.

ARCS helps each team do what it does best, while live AI decisions are governed through one control layer.

ARCS gives governance its own control layer, so product teams can keep building while policy, risk and compliance teams stay connected to how live AI decisions are controlled.

Policy people can stay involved

Rules are written in a readable form, so policy, risk and compliance teams can review what is active instead of losing the policy inside code, prompts or workflow logic.

Choose where governance runs

ARCS lets teams apply governance checks at the AI moments that matter, including model calls, tool calls, retrieval, workflow transitions and decision approval points.

Control before and after decisions

Rules can run before an action happens, after a result is produced, or when a decision needs review, so teams can prevent, inspect, record or escalate the right moments.

Capture evidence as decisions happen

Each governed decision can produce evidence showing what was checked, which rule applied, why it applied and what outcome followed.