About
AI agents for apparel operations
— governed from day one
Ariva AI builds agents for the workflows apparel companies actually run — chargebacks, EDI, compliance — and Arbiter, the governance layer that makes it safe to let them act.
What We Do
Software and advisory. Both matter.
Agents without governance are a liability. Governance without agents worth deploying is a policy document. We build both — and they work together.
Our agents handle the workflows apparel operations run every day — starting with retail chargebacks — investigating, preparing, and recommending action for a person to approve.
Arbiter, our governance layer, sits underneath every agent. It enforces decision boundaries, controls spend, routes approvals, and produces audit evidence — on every AI action, automatically.
The combination produces something neither can achieve alone: agents that are both useful and safe to deploy.
Platform
Arbiter
Governance layer for AI agents. Decision boundaries, budget controls, approval workflows, output guardrails, and audit trail — enforced at the infrastructure layer.
Explore Arbiter →Practice
AI Operations Modernization
Redesigning workflows around what agents can do — exception handling, integration readiness, and the governance to run it safely.
View AI Operations →Vertical
AI for Apparel & Fashion
Built around the systems and failure modes apparel companies actually run on — EDI, PLM, ERP, and retailer compliance.
See the Operational Environment →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
The people accountable for apparel operations and AI outcomes
Our clients are the executives and practitioners responsible for operations, technology, and AI governance in apparel and fashion companies — not the teams building AI features in the abstract.
COOs / VP Operations
Own the workflows where exceptions pile up — chargebacks, EDI mismatches, retailer compliance. Need agents that reduce manual work without disrupting operations.
CFOs / Controllers
Accountable for chargeback recovery and margin leakage. Need visibility into deductions and evidence-backed disputes, not just a dashboard.
VP Supply Chain / Sourcing
Manage supplier coordination and the systems — ERP, PLM, EDI — that connect factory to retailer. Need AI that understands that operational chain.
CTOs / VP Engineering
Responsible for platform and integration decisions. Need agents and governance that plug into existing systems, not a rip-and-replace project.
Chief AI Officers
Accountable for AI strategy and deployment at scale. Need governance infrastructure that enables agent velocity rather than blocking it.
Compliance and Risk Teams
Operating AI governance day-to-day. Need decision boundaries, audit trails, and an enforcement platform that scales with every new agent.
Work With Us
Start with an AI Operations Blueprint
We work with a limited number of clients at a time. If you're evaluating where agents and AI governance fit in your operation, the right first step is a structured conversation about your systems, workflows, and where AI would create value first.