Built from production decision systems experience
ARCS was created from experience with large scale systems, high volume payment APIs and rule driven decision logic used in real world financial flows.
Why ARCS exists
AI products are moving into real workflows where decisions can affect customers, money, operations, risk and compliance.
But applying governance to those decisions is not just a policy problem. It becomes a product and engineering problem too.
Teams need to know which rules apply, where checks should run, when review is needed, what outcome should be returned and what evidence should be captured.
ARCS exists because AI governance needs to work at the point where AI decisions happen.
It gives teams a practical way to connect governance rules to live AI decisions, adapt them to their own governance model and record evidence as decisions happen.
Why I am building ARCS
ARCS comes from a simple belief: AI governance should work inside real production systems, not only around them. When AI decisions carry real consequences, teams need more than policy documents and approval processes. They need rules that can be applied at live decision points, outcomes that can be explained and evidence that can be reviewed later.
My work over almost two decades has been close to one question: how do complex systems stay understandable, controlled and maintainable? That started with research into separation of concerns in software development, and continued through production roles across organisations including IBM, Intel, Mastercard and Boeing. At Mastercard, I worked on high volume payment APIs, fraud prevention and rule driven decision logic used in real financial flows. I am also the sole inventor on four granted fintech patents, including systems that use domain specific rule languages to author, control and audit decision logic. That background shaped how I think about ARCS: rules should be understandable, systems should be traceable and important decisions should create evidence.
AI products are moving into workflows where decisions can affect customers, money, operations, risk and compliance. But applying governance to those decisions is not just a policy problem. It becomes a product and engineering problem too. Teams need to know which rules apply, where checks should run, what context should be evaluated, when review is needed, what outcome should be returned and what evidence should be captured. Without a dedicated product for this, teams have to design, build and maintain that governance capability themselves.
ARCS is being built to bring production decision system thinking to AI governance. It gives teams AI governance software they can connect to their existing application. Teams can start with ready made rules and controls, adapt them to their own governance model, apply them to live AI decisions and record evidence as decisions happen. The aim is simple: help product, engineering, policy, risk, compliance and audit teams work from one shared control view while the AI product keeps moving.
See how ARCS applies policy in live systems
See how ARCS would fit into your AI stack and enforce policy where AI decisions happen.
