Real-Time Classification inference

Design the workflow before choosing a model

Specify the label schema and validate outputs before use. Define a timeout path and how uncertain or invalid responses reach a fallback or human queue.

Define a representative input and an explicit acceptance criterion. Keep model and prompt versions with the result so quality changes can be investigated.

Evaluate outcomes and failure modes

Measure per-class precision and recall, p95 latency, timeout rate, and cost per valid decision.

Measure under expected concurrency and class distribution. Test borderline examples and changes in input format; a model can return a valid label while making the wrong decision.

Compare candidate routes on the same held-out examples. Report how many examples were evaluated and inspect failures rather than relying on a single average score.

Roll out with a measurable cost baseline

Roll out to a limited traffic segment with a fallback. Monitor both label quality and tail latency before increasing volume or trying a different route.

Calculate cost per accepted outcome using input and output tokens, retrieval or tool fees, retries, and review effort. A lower token price is useful only if the total workflow still meets its requirements.

o10 can provide model routing for the inference steps. Your application remains responsible for workflow permissions, tool behavior, and deciding whether the final result is acceptable.