ARF AI

Trusted decision infrastructure for AI‑driven operations.

🔒 The core ARF engine is access‑controlled and not open source.
Public specification and demo UI are Apache 2.0.
Pilots: outcome‑based pricing – pay for verified risk reduction, not API calls.

Why ARF AI? Why now, not tomorrow?

Every AI‑assisted decision you don’t govern is a liability you already own.
Unreviewed, unaudited AI actions in production are like signing blank checks.

Loss aversion – The pain of a regulatory fine or operational outage is twice as powerful as the pleasure of moving fast. Avoiding a $500k incident is more valuable than gaining $200k in speed.
Ambiguity aversion – Teams hate unclear approval processes. ARF replaces tribal knowledge with deterministic, explainable outcomes.
Urgency of compounding risk – Each ungoverned AI decision increases your exposure. The longer you wait, the harder to retroactively audit.

ARF AI gives you control today. Not after the breach. Not next quarter. Now.

What ARF AI does

ARF sits between AI intent and execution. It evaluates every request in real time and returns one of three outcomes:

✅ Approve – action is safe, within policy, and uncertainty is low.
⚠️ Escalate – insufficient confidence or high impact → human reviewer.
❌ Deny – violates policy, exceeds risk tolerance, or data incomplete.

Every decision is:

Logged with a full audit trail (who, what, when, inputs, output).
Explained in plain language – no black boxes.
Signed (Ed25519) for non‑repudiation.

For whom?

Role	Value
VP of Engineering	Reduce operational risk from AI‑generated actions.
CTO / CIO	Maintain speed while adding governance. No full redesign required.
Head of Compliance	Complete, tamper‑evident audit trail for regulators.
Security Officer	Deterministic policy gates that cannot be silently overridden.
AI/ML teams	Get your models into production with trust and oversight.

Core engine (protected – no public access)

ARF’s core is a Bayesian governance engine. Key technical components:

Bayesian risk fusion

Combines online conjugate priors, offline HMC logistic regression, and optional hierarchical hyperpriors.

$\text{risk} = w_{\text{conj}}\cdot\frac{\alpha}{\alpha+\beta} + w_{\text{hmc}}\cdot p_{\text{hmc}} + w_{\text{hyper}}\cdot \mu_{\text{hyper}}$

Weights adapt with data volume. Posterior → 90% HDI for uncertainty.

Expected loss minimisation

Chooses the action that minimises expected cost:

$\begin{aligned} L_{\text{approve}} &= \text{COST\_FP}\cdot R + \text{COST\_IMPACT}\cdot b_{\text{mean}} \\ L_{\text{deny}} &= \text{COST\_FN}\cdot(1-R) + \text{COST\_OPP}\cdot v_{\text{mean}} \\ L_{\text{escalate}} &= \text{COST\_REVIEW} + \text{COST\_UNCERTAINTY}\cdot\psi \end{aligned}$

Execution ladder (Rust)

Mechanical gates: license → confidence → risk → rollback → causal. Deterministic, auditable.

Lyapunov stability

Quadratic candidate $V(x,r) = \alpha r^2 + \beta|x - x_{\text{des}}|^2$ guarantees healing actions converge.

Cryptographic signing

Ed25519 signatures for HealingIntent – non‑repudiable governance.

Public specification (Apache 2.0)

Data models & API contracts – InfrastructureIntent, HealingIntent, RiskScore, GovernanceLoop
Mathematics – Full derivations, Lyapunov proof
Governance loop – Constants (COST_FP, COST_FN, EPISTEMIC_ESCALATION_THRESHOLD)

Live demos (mock data only)

Demo	Description	Link
Risk Dashboard	Adjust priors, see HMC simulation, semantic memory retrieval	Launch
Sandbox API	Mock FastAPI endpoint, interactive `/docs`	Try API

All demos use simulated responses. Real enforcement requires pilot access.

Public repositories (Apache 2.0)

Repository	Description
`pitch-deck`	Public overview and investor materials

Private repositories (Core Governance Engine, API Control Plane, Enterprise Extension) are pilot / enterprise only.

Access model

Layer	Availability	Purpose
Public sandbox	Mock responses only	Demonstration and evaluation
Pilot program	Invitation‑only, time‑limited	Validate use case with controlled access
Enterprise core	Protected production engine	Commercial deployment, full enforcement

Pilot access – outcome‑based pricing

You don’t pay per API call. You pay for verified risk reduction.

👉 Apply for pilot access →

When applying, include:

Organization name
Use case (e.g., infrastructure change review, AI‑assisted operations)
Expected volume (approx. evaluations per month)
Cloud environment (AWS, Azure, GCP, on‑prem)

Pilot is time‑limited and free for qualified organizations. No commitment – just validation.

Why enterprises choose ARF AI

Deterministic – identical inputs → identical decisions.
Explainable – every decision includes a human‑readable justification.
Auditable – complete, tamper‑evident audit trail.
Cloud‑agnostic – runs anywhere (AWS, Azure, GCP, on‑prem).
Secure – SSO, RBAC, SOC2‑ready design.
Preserves speed – adds governance without forcing a full system redesign.

Trust & compliance

ARF is architected for regulated environments:

Tamper‑evident audit trails (regulatory review ready).
Mechanical enforcement – policy gates cannot be bypassed.
Explainable reasoning – suitable for third‑party audits.
Supports GDPR, SOC2, ISO27001 alignment.

Product principles

Governance should be deterministic where possible.
High‑impact decisions must be explainable.
Human review remains available for uncertainty.
Auditability is built in, not retrofitted.
Enterprise deployment must not require abandoning existing infrastructure.
Commercial terms reflect actual value delivered (risk reduction).

Short version

ARF AI is the decision layer between AI intent and production execution.
It evaluates, decides, and logs – so you can move fast without losing control.

Don’t wait for the incident that forces governance. Govern every AI decision today.

Legal & license

Core engine – proprietary, trade secret. No public access.
Public specification and demo UI – Apache 2.0.
All materials – may not be copied, redistributed, reverse engineered, or used for AI training without written permission from ARF Foundation.