← All insights
AI|
Shipping AI features with guardrails people will actually run
Retrieval, evaluation, and rollout patterns we use so model-backed features stay useful when inputs drift and stakes are high.
Production AI is less about the flash demo and more about the operating loop: evaluation sets that match real tasks, monitoring for regressions and abuse, and rollback paths when a prompt or model version misbehaves.
We combine retrieval and policy constraints where appropriate so answers stay grounded, and we keep humans in the loop for edge cases instead of pretending automation is complete.
If you are moving from prototype to customer-facing features, we recommend budgeting time for evaluation harnesses and incident playbooks, not only model tuning.