The intelligence layer for
auditable, active governance.
Arcus builds AI that can defend its decisions. The reasoning trace is the computation itself — not a story the model tells afterward — so every step is inspectable and every action auditable. Built on right-sized open models that run on hardware you own.
Governance you can prove, not just claim.
Most AI governance platforms record the reasoning the model reports about itself — inventory it, validate it, file it. That is passive governance: the best filing cabinet still files whatever story the model tells. Regulators ask something else entirely — how the decision was reasoned, where it was challenged and caught, and why the institution stands behind it. Active governance answers from within the decision: the trace is the computation, not a report about it.
Auditable. Efficient. Sovereign.
You cannot audit a black box you rent. Efficiency is what makes ownership affordable, and commodity hardware is what makes it sovereign — the same bet, three faces. While inference is sold below cost, no one has to ask which decisions deserve it, or who owns the answer. The subsidy will end. We build for the market on the other side of it.
Auditable
Every inference inspectable, every action auditable — the reasoning trace produced as the decision is made, not reconstructed after. The layer regulators, auditors, and courts demand.
Efficient
Right-sized open models, not frontier overkill. Optional while inference is subsidized — decisive after it.
Sovereign
Runs on premises, on high-end commodity hardware. No hyperscale rent, no kill switch, no cluster required. Air-gapped where it has to be.
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 engine, a sequence of regulated markets.
We win a beachhead with a real result, then point the same governance gate at the next regulated domain. Each new vertical is a new ruleset — not a new build.
Reg E dispute adjudication
Full fraud-dispute determinations with evidence — closed-loop, fully auditable, in about a minute on an open-weight model. This is the beachhead, live today.
Coverage governance
The gate that checks Reg E rules checks a carrier’s coverage clauses — sold to the party carrying the liability, before a claim materializes.
Model Risk Management
Validate model packages against SR 26-2, the Fed/OCC model-risk guidance rewritten for AI in 2026 — from the same engine that earns credibility in adjudication.
See what the examiner sees.
Every IRG determination ships as a Regulator Dossier: an examiner-ready record of a single AI decision, generated with the decision itself. We produced one for a live Reg E overdraft-fee dispute — enter your work email and we’ll give you the full 21-page sample.
- Provenance — model, prompts, graph, and rule corpus pinned by SHA-256; re-hash and verify byte-equality
- Integrity seal — every model call sealed in a hash-linked chain; tampering is detectable from genesis
- Reasoning trace — the determination as a formal, step-by-step proof, plus every node’s actual output
- Citation provenance — each claim resolved to a verified regulation or evidence artifact
- Compliance timeline — the §1005.11 statutory clock, milestone by milestone
- Standards mapping — NIST AI RMF and model-risk guidance, evidenced line by line
- Known limitations & attestation — what the dossier does not prove, and the reviewer’s signature surface
Get the sample dossier
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Thanks — here is the sample Regulator Dossier for case-009-overdraft-fee-dispute.
Download the PDFOne 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 26-2: Model Risk Management
The Fed/OCC model-risk guidance rewritten for AI in 2026, superseding SR 11-7. Validation evidence, effective challenge, and ongoing monitoring — Arcus produces all three from the same trace, under either standard.
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.
Read →Let’s talk about governed reasoning.
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 — we are onboarding design partners in insurance coverage governance and model risk management now.
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