Models and AI systems drift, degrade and get quietly worked around long after the launch review. We put the monitoring, evaluation, versioning and governance around them that keeps them useful, safe and accountable in production.
We log inputs, outputs, versions and cost for each call, so live behaviour can be reconstructed rather than guessed at.
Drift monitors, regression suites and graded evaluation sets run continuously, with human review kept wherever automated scoring cannot be trusted.
Models, prompts, datasets and policies move through the same gated release path, so any change can be traced, approved and rolled back.
Models drift, prompts regress, and a system that was accurate at launch is not automatically accurate a quarter later. The work here is the discipline that catches that: evaluation that keeps running after go-live, versioning that makes a change reversible, and monitoring that tells you which behaviour moved and when.
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