Resources
Decision notes for private AI buyers.
See the hardware, model, and ownership decisions that shape a private AI deployment before it reaches production.
Planning note
Hardware planning
The sizing questions we review before recommending hardware or pilot scope.
- Expected model class, latency goals, and concurrent users set the starting hardware tier.
- Document volume, indexing strategy, and backup retention drive storage requirements.
- Power, cooling, network placement, and physical access affect what will work on site.
- Assumptions should be documented so the pilot can be benchmarked before production.
- Hardware remains customer-owned and should be reviewed against the pilot plan before purchase.
Decision record
Model policy
How model choices, limits, license constraints, and refresh plans are recorded.
- License terms must support the customer's commercial use case before deployment.
- Quality, speed, and memory use should be validated against approved documents and workflows.
- Each environment needs a record of which model is deployed, why it was selected, and where it falls short.
- A controlled refresh path prevents model updates from becoming an unmanaged change.
- Any hosted or external model call needs explicit customer policy review first.
Checklist
Security and handoff
The ownership and access questions to settle before production use.
- Who owns admin credentials, and where are they stored?
- How is temporary remote access approved, logged, and removed after handoff?
- Are AI services reachable from the public internet? The default answer should be no.
- Who runs backups, how often, and when was restore last verified?
- What is documented for handoff, and who owns updates afterward?
Want this handled with you?
The assessment turns these topics into a plan.
We review your use cases, documents, hardware, and security boundaries with you, then return a recommended pilot path.