Insights
PrivateMind Workspaces
Kimi walks through creating, deploying and sharing an enterprise app inside a PrivateMind quant workspace.
The number one topic on customer's minds heading into the 2027 budget season is how firms can build from their investment in AI (or experimentation with AI) to realise impactful AI transformation strategies. How to not only build impactful applications, but deploy them in production in a secure and scalable fashion.
Kimi talks us through how to create, deploy and share an enterprise app in a PrivateMind AI quant workspace. Simple as that.
In summary, PrivateMind Workspaces:
- Everything in one environment: VS Code, terminal, Harness and Jupyter share one directory, and allocation of Compute including CPU, GPU and Micro Virtual Machines.
- Keys minted for you: Each workspace gets it's own PrivateMind API key automatically, so agents and notebooks can call models with minimal setup.
- Agents under control: Coding agents run inside an environment you govern.
- CPU or GPU: Start with a CPU workspace for everyday development or a GPU workspace when you need to train, evaluate or serve.
- Port Previews: Start a web server in the terminal and open it from a stable URL, behind the same access checks as the workspace.
- Collaboration: Share a workspace with a colleague for pairing or review. Everyone uses their own identity, so access stays auditable.
Want to see how this would work with your firm's data?
