No public model drift
Your team is routed through a managed private model path instead of a random collection of consumer AI accounts spread across the business.
Give your team a private AI workspace with its own hostname, managed model route, and no “figure out cloud infrastructure first” tax. Built for firms that want the usefulness of modern AI without public model sprawl, prompt leakage, or training on sensitive business data.
Most “AI tools” quietly turn your team into infrastructure operators. This one does the opposite: it packages privacy, isolation, and usable chat into something a normal business can actually adopt.
Built for cautious teams that still want speedYou should not need to read cloud diagrams to know whether your AI setup is safe enough for real work. These are the controls we surface in plain language.
Your team is routed through a managed private model path instead of a random collection of consumer AI accounts spread across the business.
Each customer workspace gets its own tenant URL and runtime boundary, so the product feels like yours instead of a shared lobby with a nicer skin.
Teams that are not deeply technical still get a clean sign-in, a clean chat surface, and a manageable route into production use.
No. The product promise is a private route and a private workspace, not a consumer chatbot account wearing enterprise language.
No. The platform handles provisioning and the secure workspace surface so your team can adopt AI without becoming infrastructure operators.
Each workspace is provisioned behind its own customer-facing domain route.
Tenant routes are created automatically, with real certificates and a reproducible deploy path.
We trimmed this down to the flow a non-technical buyer actually expects: create account, provision workspace, open chat.
Use email and password. No need to think about tokens, infrastructure, or model paths for the normal workflow.
Click one button. The platform creates the tenant, wires the model route, and prepares the customer URL.
Once status turns active, your team lands in a private chat experience instead of another generic admin console.
Advisory, finance, legal, operations, healthcare-adjacent, and internal teams that cannot casually dump work into public tools.
The goal is operational utility with a better privacy posture, not impressing engineers with a maze of settings.
Instead of five shadow AI subscriptions and no policy story, you get one product you can actually roll out.
Keep the buying choice straightforward: one clear starting point for teams who need private AI now, and one higher-touch path for larger rollouts.
For firms that want a secure, customer-ready private AI workspace without building their own platform layer first.
Your team gets its own customer-facing workspace route.
The AI backend is already wired so you can focus on adoption, not plumbing.
Create account, provision, and open the workspace when it turns active.
For businesses that need custom domains, rollout guidance, or a more deliberate privacy and operating model.
Align the workspace with your existing customer or internal domain approach.
Move from “we want private AI” to an actual deployed team surface with fewer wrong turns.
Useful when security, leadership, and frontline teams all need a coherent story.
No. The product is designed so normal business teams can use private AI without turning into prompt engineers or cloud operators.
Yes. The operating model is one customer workspace with its own URL and runtime route, not one shared public app for everyone.
No. It gives you a usable private AI surface quickly. That is the point: less chaos, more controlled adoption.
Yes. Start with the managed workspace flow. Add domain or rollout sophistication only when it is actually needed.
That is enough to prove whether private AI will actually work inside your business. You do not need a larger thesis before the first useful deployment.