Give your whole team AI, without giving away your data
A private, multi-model AI workspace your security team can actually approve. Every major model, projects, agents, and your own knowledge in one place, with nothing training a public model and every message logged.
Consumer AI at work is a data leak with a login
People are not being careless. The tools are genuinely useful, so staff reach for whatever is easiest and paste in whatever gets the job done. The problem is not the intent, it is what happens to the data afterwards.
Your data walks out
A pasted contract, a customer list, a snippet of source code. Once it is in a consumer tool, you have no way to pull it back.
It trains on your secrets
What people type into public models can be used to improve them, which means your confidential input can resurface somewhere you never intended.
No audit, no answers
When an auditor or a customer asks what your AI was used for, "we are not sure" is not an answer you want to give.
Blocking just hides it
Ban the tools and the usage moves to phones and home accounts, where you cannot see it at all. The exposure does not go away; your visibility does.
A real AI workspace, not a locked-down box
Security tools usually make AI worse to use, so people go around them. This one is better than the consumer apps it replaces, which is why people actually stay inside it.
Every model, one login
ChatGPT, Claude, Gemini, and more behind a single secure login. Pick the best model for the task without a new contract or a new password.
Projects
Group related work into projects with shared files, context, and history, so a piece of work carries its own memory.
Knowledge and RAG
Ground answers in your own documents, wikis, and data sources, so the AI answers from what your company actually knows.
Agents
Hand off multi-step tasks to agents that can plan, use tools, and come back with the work done.
MCP connections
Connect your tools and systems through the Model Context Protocol, so the AI can act where your work already lives.
Model debate
Put models against each other on the same question and let them cross-check, so you get an answer that has been argued, not just generated.
Hallucination checks
Automatic flags when an answer is not grounded in a trusted source, so people know exactly what to double-check.
Data protection
Sensitive data is detected and redacted before it reaches a model, and nothing your team types trains a public model.
Audit and admin
Every message is logged to a record you can stand behind, with SSO and policies your security team controls.
Approved does not have to mean expensive
Per-seat AI licenses mean paying for every employee whether they use the tool or not, and standardizing on a single vendor means people quietly use the others anyway. Both roads lead back to shadow AI.
Unseen gives everyone access to every major model on usage, not seats. You stop paying for shelfware, you stop forcing people toward the unsanctioned tools, and you keep one place where all of it can be governed.