AI Governance
Govern every agent. Control every byte of enterprise data.
AI agents are only useful if they can reach your data, and only safe if they can’t reach all of it. ues.io makes sure users and the agents they use see only what they are permitted to see, with field-level permissions, configurable guardrails and a complete audit trail.
The control plane for enterprise AI.
Employees can already use frontier models to build applications on their own. The risk isn’t the model. It’s that nobody governs what it can see, what it costs or who used it. ues.io is the control plane between your people, agents, models and data.
| AI used directly | With ues.io | |
|---|---|---|
| Model choice | One provider | 14 providers, swappable |
| Usage and cost | Little visibility | Tokens and cost per user, app and developer workspace |
| Data access | Whatever the user pastes in or connects | Field-level permissions, enforced for agents |
| Guardrails | Not applied | Configurable per model and per call |
| Audit | Scattered or none | Prompts, responses, user, model, cost |
| Budgets | Not available | Set per user; calls stop at the limit |
Governance built into every request.
Permissions & Profiles
Agents see what users see.
Nothing more.
Every agent runs with the permissions of the person using it. Permission sets control row-level security down to individual fields.
Security settings are set once and enforced across apps, workflows and agents. If a user can’t see a record or field, neither can their agent.
Guardrails
Protect enterprise data.
Per model, per call.
Builders configure guardrails per model and per call, so trusted and external models can follow different rules and sensitive requests get tighter controls.
Combined with field-level permissions, enterprise data is not passed to an AI beyond what you allow.
Audit Trail & Budgets
Know who used what.
Budgets that hold.
Every AI call records the user, agent, model, prompt, response, tokens and cost, across all users, all apps and each development workspace.
Set budget levels per user. When a budget is reached, calls stop.
Build, connect and orchestrate with agents.
MCP Connectivity
Connect to MCP.
On your terms.
Connect ues.io to MCP servers and bring external tools and systems into governed workflows.
Every connection runs inside the same permission model as the rest of the platform.
MCP Connectors
Publish your app.
Make it MCP-enabled.
Build an app on ues.io and enable it for MCP in a few steps. It works as a connector other agents can use, with chatbot access for your users.
The app abides by the permissions allocated to each user, including when an agent acts for them.
Agent Development
Build on ues.io
with your own agents.
Use the ues.io CLI and the ues.io connector for agents so your coding agents can build directly on the platform.
Agent-built apps follow the full enterprise development lifecycle, with Git integration and version control, and run inside the platform’s permissions and governance.
Model Choice
14 LLM providers.
Swap models anytime.
Orchestrate agents across 14 LLM providers and swap models without rebuilding. Choose by cost, performance or data policy, and your governance stays the same.
Automatic selection of the most cost-effective model for each task is coming soon.
Bring Your AI Apps
Already built with AI?
Add governance.
If your team has built a vertical application with AI and now needs governance, you can move it into ues.io.
Keep what you built and gain permissions, guardrails, audit and cost control.
AI governance questions.
Can an agent see data its user can’t?
No. Agents act with their user’s permissions, enforced down to field level.
Can we control AI spend?
Yes. Track tokens and cost across users, apps and workspaces. Budgets stop calls when reached.
Can we see what was asked and answered?
Yes. The audit trail records prompts and responses with the user, model, tokens and cost.
Can we apply different guardrails to different models?
Yes, per model and per call.
Do we have to use one LLM?
No. ues.io supports 14 providers and lets you swap between them.
If we publish an app as an MCP connector, who can see its data?
Only what each user’s permissions allow. The app abides by the permissions allocated to the person using it, including when an agent calls it for them.
We already built an app with AI. Can we add governance?
Yes. You can move it into ues.io.
Does ues.io connect to legacy systems?
Legacy integration is coming soon.
AI models supported.
14 model providers, one governance layer. Swap between them without rebuilding.
- Amazon Bedrock
- Anthropic
- OpenAI
- Google Gemini
- Mistral AI
- xAI
- DeepSeek
- Cohere
- Perplexity
- Groq
- Cerebras
- Fireworks AI
- DeepInfra
- OpenRouter