AI systems make decisions.
We make those decisions auditable.
Arcus builds infrastructure for AI that has to explain itself. Every decision is broken into traceable steps: named, typed, verified, and scored. Built for industries where the model’s answer is only as good as the reasoning behind it.
The difference between logging and governance.
Most AI governance platforms track which model was called, when, and by whom. That is asset management, not governance. Regulators do not ask which model you used. They ask how the system arrived at its decision. That question needs more than a call log. It needs a reasoning structure.
Three layers of reasoning infrastructure.
The Arcus Protocols define how AI thinks. Every decision is structured, every output is scored, and the reasoning architecture can be shaped by domain experts without engineering support. Auditability is not a feature added on top. It is how the system is built.
Iterative Reasoning Graphs
IRG breaks AI decisions into typed, traceable steps: generation, retrieval, verification, evaluation, and synthesis. The system works through them iteratively until a defined standard is met. Drafts are discarded. The reasoning behind them is not.
Epistemic Integrity Engine
EIE scores every reasoning output across four dimensions: factual accuracy, logical coherence, source fidelity, and claim support. The result is a continuous integrity metric that regulators, auditors, and insurers can reference. Not a binary pass or fail. A calibrated read on the quality of the reasoning itself.
Graph Definition Language
GDL gives teams a way to define how their AI thinks. Ten composable primitives cover the full range of reasoning operations. Domain experts can configure and adjust reasoning structures on their own, without engineering support or knowledge of graph theory.
Above the model layer. Beside the GRC platform.
IRG does not replace your governance platform or your model provider. It connects them. Model calls are orchestrated within a structured reasoning process, and every step produces the traces and scores that turn compliance documentation into something auditors can actually use.
One protocol. Every compliance surface.
Every jurisdiction asks for the same things: risk documentation, reasoning transparency, audit trails, quality management. IRG produces all of them from the same underlying trace. The regulatory templates and prompt sets change. The protocol does not.
EU AI Act: Articles 9–15
High-risk AI systems require technical documentation, automatic event recording, risk management, and human oversight. Credit scoring, employment, healthcare, insurance.
SR 11-7: Model Risk Management
Federal Reserve guidance requiring banks to validate, document, and govern models used in decision-making. The standard MRM framework for US financial institutions.
Colorado AI Act
Impact assessments, documentation of decision-making, transparency obligations for high-risk AI in employment, lending, insurance, and housing.
Cross-jurisdictional
Institutions operating across borders adopt the strictest standard. EU compliance subsumes most frameworks. One EIE deployment covers all surfaces.
“Scaling models improves the constants. Graph design shifts the Pareto frontier. The former is expensive and subject to diminishing returns. The latter is a design choice with compounding returns as the discipline matures.”— Cognitive Engineering: A Formal Framework, Arcus Research
Grounded in theory, built for production.
The formal theory behind cognitive engineering. Covers the R/K/G framework, four governing laws, topology classification, circuit complexity, and cost models."
Cognitive Engineering v0.2
The formal theory behind cognitive engineering. Covers the R/K/G framework, four governing laws, topology classification, circuit complexity, and cost models.
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The organizations that get this right are the ones that built for accountability from the start. If that is where you are headed, we should talk.
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