Giving startups the infrastructure to innovate.

01 / The business challenge

A better foundation for the work ahead.

A venture lab supporting multiple startups needs to balance access, budgets, and competing compute requirements. Separate cloud accounts can make spending difficult to see and leave promising experiments waiting for resources.

Why consider private AI?

A shared private environment could provide a common foundation for prototyping, evaluating open-weight models, and running eligible inference jobs. A managed allocation model helps turn limited compute into a resource more teams can use.

02 / The Corsantic approach

Understand first. Build with purpose.

1. Understand each team’s models, datasets, expected usage, and isolation requirements.
2. Design quotas, scheduling, separate workspaces, and an accountable process for resource allocation.
3. Pilot with a small group of teams, then use utilization and feedback to plan expansion.

Deployment approach

A shared GPU environment could include isolated workspaces, identity-based access, usage visibility, and scheduled compute windows. Model development would be scoped to the capacity available; large foundation-model training is not assumed.

03 / What success would look like

Outcomes to validate together.

Validation before expansion

The pilot would measure queue time, successful experiments, utilization, and team satisfaction while testing workspace isolation.