Why most AI systems plateau — and why loop services AI doesn't
Most production AI systems plateau because they're trained on a static dataset and deployed forever. Loop services ai design changes that: the system observes its own decisions, captures outcomes, and feeds them back into evaluation and retraining.
What we build
- Outcome capture pipelines wired into the production workflow.
- Continuous evaluation harnesses comparing model versions on live data.
- Automated retraining triggers based on drift or outcome degradation.
- Shadow and canary deployment infrastructure for safe rollout.
- Human-in-the-loop labelling for the cases AI flags as uncertain.