AI engineering instead of AI experimentation.
Test datasets, evaluation harnesses and failure-mode analysis that turn AI behaviour into something measurable.
- Custom systems engineeringfrom $15k
- Productized systems$5k–$40k+
- Arvanord Ops coveragemonthly
You cannot operate what you cannot measure. Evaluation engineering defines desired behaviour, builds test cases from realistic work, runs datasets against the system and finds failure modes before your users do.
Evaluations become part of the system: they run on changes, guard deployments and document what the system is expected to do.
- Behaviour specifications
- Evaluation datasets from real cases
- Automated evaluation harnesses
- Failure-mode analysis & reporting
- Regression gates for changes
How this is engineered.
Established patterns, chosen for reliability over novelty — adapted to the environment rather than invented for it.
Define
What the system must do, never do, and how it should behave when uncertain.
Test
Cases drawn from realistic workflows, including the awkward ones and the edge cases.
Measure
Output quality scored against the specification, with failure modes categorised.
Improve
Fixes verified against the same dataset — no regression, no guessing.
Often engineered together.
Systems are rarely one discipline. These capabilities most often share an architecture with evaluations.
Engineer evaluations into your operations.
Every system starts with the same first step: understanding the work.