Stage 01
Discovery
Understand the work before choosing models or hardware.
- Business objectives
- Users
- Workflows
- Data types
- Existing infrastructure
- Security requirements
- Budget
- Timeline
How It Works
A careful path from idea to maintained infrastructure
Private AI projects are easiest to understand when each stage answers a different question. The process stays small enough to evaluate and documented enough to support.
Stage 02
Architecture
Define where the system lives, what it touches, and how it is operated.
- Local, hosted, or hybrid
- Model options
- Hardware requirements
- Storage
- Authentication
- Interface
- Logging
- Backup
- Network boundaries
Stage 03
Prototype
Test a narrow useful case before scaling assumptions.
- Small initial use case
- Limited document set
- Evaluation questions
- Performance tests
- User feedback
- Failure analysis
Stage 04
Deployment
Turn the tested design into a documented operational system.
- Production configuration
- User access
- Documentation
- Data loading
- Security review
- Training
- Handover
Stage 05
Ongoing Support
Maintain the whole system as models, users, and knowledge change.
- Updates
- Monitoring
- Model changes
- RAG maintenance
- User management
- Capacity planning
- Incident support
A controlled next step
Put a real use case through the process.
Describe the task, users, information, and infrastructure. We can determine whether discovery or a readiness assessment is the right first stage.