Operations
Managed AI Operations
Keep production AI reliable after launch.
Evals, monitoring, cost control, and steady improvement after the system is live.
System shape
How we approach it
After launch, quality, cost, and models drift. We own the eval loop, monitoring, upgrades, and incident path so the system stays trustworthy.
Where this creates value
Jobs this outcome is hired to do.
- 01Ongoing evaluation and quality checks
- 02Observability and incident support
- 03Model upgrades and prompt/version management
- 04Cost optimisation and reliability improvements
What engineering includes
The build work behind the outcome.
- AI evaluations and observability
- Production monitoring
- Model routing and upgrades
- Cost and performance optimisation
- Security and reliability reviews
How delivery works
From first map to something live you can measure.
01
Baseline the live system
Quality, latency, cost, and incident history as the operating starting point.
02
Instrument & alert
Hooks for failures, drift signals, and review queues your team can act on.
03
Operate the loop
Weekly or monthly eval reviews, upgrades, and fixes with clear ownership.
04
Improve deliberately
Prioritise changes by user impact and risk, not by chasing every new model.
Example use cases
Concrete jobs this outcome is built for. Not attributed client claims.
01
Quality regression watch
Run eval suites on a schedule, catch answer or agent regressions before users do.
02
Cost & latency control
Track spend and response times, then tune routing, caching, or prompts when limits slip.
03
Model & prompt upgrades
Test new models or prompt versions against your suite, then roll out with a rollback path.
What you walk away with
Typical deliverables for a scoped engagement.
Have a use case for this outcome?
Bring the workflow or product need. We will say whether a sprint, pilot, or a different path fits.