MLOps & AI Infrastructure
The unglamorous layer that decides whether an AI system survives its first six months in production.
Ongoing, or 4–6 weeks to establish
Most AI projects fail after the demo, not before it. We build the operational layer — versioning, monitoring, evaluation, cost controls and rollback — so a model in production stays accountable and can be improved rather than quietly degrading.
What this solves
- Models degrading silently after deployment
- No way to reproduce which version produced which output
- Token and inference costs nobody is watching
- No rollback when a new version is worse
What you get
- Model and prompt versioning
- Production monitoring and alerting
- Evaluation harness with regression tests
- Cost tracking and budget controls
- Rollback and canary deployment
Technology
DockerRailwayAWSAzureGrafanaSentry
Talk to the engineers who build it
Tell us the problem and we'll tell you whether mlops & ai infrastructure is the right answer — and what it would take.