Your Teams Already Code with Tools Nobody Has Approved
A personal account opened on the night of a release, an IDE extension installed to save an hour: proprietary code and customer data end up in external models. The day an auditor asks what was sent and under which rule, nobody has the answer. Argy brings that usage back inside a governed perimeter, with Argy Code and the LLM Gateway, and gives you back the trail that was missing.
Shadow AI Doesn’t Start with Bad Intent
It starts with a gap: your teams move faster than IT governance, and each one fills the gap with whatever tool is at hand. The cost of that shortcut stays invisible until the day it comes due. Proprietary code and internal documents full of customer data leave your perimeter for external models, unfiltered and unchecked.
Nobody knows which prompts were sent, which models answered, or which decisions followed. Accounts multiply, spending escapes any FinOps oversight, and the AI Act question lands at the worst possible moment: an organization that cannot trace its usage faces potential non-compliance with governance and auditability requirements, which it may only uncover during the audit rather than before.
Bring Every Use Back Inside a Perimeter You Control
It starts with a single entry point. The LLM Gateway centralizes calls to models, enforces your quotas and filters, and routes across multiple providers. Every request leaves its mark in the audit logs: what used to go through personal accounts now takes a path your CISO can see.
Your developers keep their coding assistance, now subject to your organization’s controls: quotas, filters, and logging. Argy Code is available today for work with a governed agent; Argy Workbench (coming soon) will offer a governed workspace without Shadow AI.
What works gets repeated instead of reinvented: Module Studio lets you build versioned, governed golden paths. Governance and traceability are built in and aligned with the AI Act, so when the auditor asks, you finally have something to show.