New white paper: AI-built apps need a control plane – Download it here →

Every AI agent answers to a person.

UES.IO runs every agent with the identity and permissions of the person it works for, down to individual rows and fields. Every action is recorded. Every budget is enforced. All of it runs on your infrastructure, with the models you choose.

The Secure Agentic Enterprise Platform
for Building and Controlling AI-native Applications,
Workflows, Data, MCP Connectors & Agent Orchestration.

Full Stack

Multi Tenant

Managed Packages

Self Host

MCP Native

Swiss Made · Self-hosted or private deployment · Available on AWS Marketplace

platform

How it works

Accountability built into every agent action.

Agents on UES.IO never hold access of their own. They borrow it from a person, and every action leaves a record. Governance isn’t a layer added on top. It’s how the platform works.

Read about AI governance →

Your platform keeps running on your terms.

Run It Where You Choose

Container-based and infrastructure agnostic. Deploy in your own data centre or private cloud, including the air-gapped Sovereign edition.

Your Apps Live in Your Git

Applications, workflows and agents are versioned as code, with a CLI and full rollback. They stay in your repositories, under your control.

Switch Models, Not Platforms

Choose from 14 model providers or bring your own. Change providers without rebuilding, with the same guardrails, audit and budgets.

Every Action on Record

User, agent, model, prompt, response, tokens and cost, in one audit record.

Budgets That Hold

Set an AI budget per person. Calls stop at the limit, not at the invoice.

One Permission Model

The same row- and field-level rules govern apps, workflows, agents and MCP tools, checked on every action.

Discuss your deployment

Built for teams and systems that can’t afford to fail.

What our clients say

Tell us what you’re building. We’ll tell you if UES.IO is the right fit.

We don’t need a lengthy requirements document. A short conversation about your environment, your constraints, and what you’ve tried before is usually enough.

Talk to us