AI platform setup
Set up the foundation your team builds AI on: model access, environments, secrets, a deployment pipeline, logging, evaluation, and cost controls.
Timeline agreed with scope. Fixed price agreed before work starts.
What you receive
- Development and production environments with model access, secrets, and permissions set up under your accounts
- A deployment pipeline your team can run, with environment separation and rollback
- Logging and an evaluation harness so changes to prompts, models, or data can be checked before release
- Usage and cost controls with alerts, so spend stays visible and bounded
- Setup notes and a working session with the engineers who will use it
Scope and limits
We agree the cloud accounts, model providers, and repositories in scope. The platform is built in your accounts and handed over; we do not host it for you. Building the applications on top of it, and operating it afterwards, are separate scopes.
When this is not the right engagement
If you have one use case and no plans for more, a platform is more than you need. A proof of concept or a production build sets up only what that system requires.