About
Governance infrastructure
for enterprise AI
Ariva AI builds enterprise governance software and operates a high-trust advisory practice. We help enterprises govern AI before regulators, incidents, or board inquiries force the issue.
What We Do
Software and advisory. Both matter.
Governance frameworks without enforcement mechanisms are aspirational. Enforcement tools without governance design are compliance theatre. We do both — and they work together.
Arbiter is our governance operating system for enterprise AI. It enforces policy, controls spend, routes approvals, and produces audit evidence — on every AI call, in production, automatically.
Our advisory practice designs the governance architecture that Arbiter enforces. We assess AI deployments, identify control gaps, design policies and approval workflows, and build the operational resilience infrastructure that keeps enterprises running when AI systems behave unexpectedly.
The combination produces something neither can achieve alone: governance that is both principled and operational.
Platform
Arbiter
Enterprise AI governance software. Policy enforcement, budget controls, approval workflows, output guardrails, and audit trail — enforced at the infrastructure layer.
Explore Arbiter →Practice
AI Governance Advisory
Governance assessment, risk discovery, control design, and Arbiter implementation advisory for operational enterprises.
View Advisory Practice →Practice
Operational Resilience
Failure mode analysis, human fallback design, incident response playbooks, and recovery architecture for AI-dependent operations.
View Resilience Practice →Our Approach
Operator-led.
Not analyst-advised.
Ariva AI was founded by operators who have deployed AI systems in enterprise environments — and lived with the consequences of insufficient governance.
We have seen what happens when AI governance exists only on paper: model drift that wasn't caught, incidents that escalated because fallback procedures didn't exist, audit inquiries that couldn't be answered because evidence wasn't collected.
That experience shapes how we work. We don't produce governance frameworks that collect dust. We design controls that can be enforced — and build the software that enforces them.
We build what we recommend
Every governance control we design for clients is implemented in Arbiter. If we can't enforce it in production, we don't recommend it.
Evidence over intent
Governance that can't produce evidence isn't governance. We design for auditability from the start — not as an afterthought.
Operations first
Governance that breaks operations doesn't survive. We design controls that enterprises can live with — sustainable, proportionate, and operational.
High-trust engagements only
We work with a small number of clients at a time. Our advisory practice is not a volume business — it's a high-accountability practice.
Who We Work With
Enterprise leaders accountable for AI outcomes
Our clients are the executives and practitioners responsible for AI governance, risk, and operational integrity — not the teams building AI features.
Chief Risk Officers
Responsible for AI risk governance and board reporting. Need evidence-based compliance posture without operational disruption.
Chief AI Officers
Accountable for AI strategy and deployment at scale. Need governance infrastructure that enables AI velocity rather than blocking it.
Chief Technology Officers
Responsible for AI engineering and platform decisions. Need governance enforced at the infrastructure layer without rewriting applications.
General Counsel
Managing legal and regulatory exposure from AI systems. Need documented controls and audit-ready evidence for regulatory inquiries.
Audit Committees
Board-level oversight of AI risk. Need independent assessment of AI governance posture and evidence of control effectiveness.
Compliance and Risk Teams
Operating AI governance programmes day-to-day. Need practical controls, runbooks, and an enforcement platform that scales.
Work With Us
Start with a governance review
We work with a limited number of enterprise clients at a time. If you're evaluating AI governance options, the right first step is a structured conversation about your current posture and what adequate governance looks like for your organization.