All services
MLOps and production deployment
The unglamorous half that decides whether any of it survives.
What this actually involves
A model in a notebook is not a product. Production means versioned prompts, regression evaluation on every change, cost ceilings that hold, and enough observability that a bad output can be traced to the run that caused it.
- CI/CD for prompts, models, and retrieval indexes
- Continuous evaluation wired into the deploy pipeline
- Cost, latency, and quality monitoring with alerting
- Audit trails, PII handling, and governance documentation
When it fits
- A prototype works and now has to survive real traffic and real users.
- You pay model bills you cannot explain, or output quality drifts and nobody notices.
- Legal or security wants an audit trail before they approve the launch.
When it does not
- There is no prototype yet. Build something worth operating first.
- You want monitoring for a system nobody plans to maintain.
- The model is a one-off analysis that will never run again.
What we need from you
- Access to the current code, prompts, and deployment setup.
- Your traffic numbers and your monthly model spend.
- Your security and compliance requirements in writing.
Ready to move from AI experiments to AI in production?
A thirty-minute call. We assess the use case, name the risks, and tell you honestly whether we are the right fit.