source/docs/INTEGRATION_CHECKLIST.md

Integration acceptance criteria

Use this short list to decide whether the primitive fits your existing service.

1. Express the statistic as (N0 + sum(w*v))/(D0 + sum(w)), with nonnegative bounded weights and a strictly positive denominator floor.
2. Establish valid input intervals. If your feasible set is correlated, a surrounding independent box gives sufficient positive guarantees; its negative witnesses may be relaxation artifacts.
3. Choose exact for the unconditional integer-input numerical contract, or choose filtered explicitly under the published IEEE/NumPy premises.
4. Prepare static values once. For the supported fast use case, submit fresh mass arrays and one threshold vector; keep the running process alive.
5. Handle false results as coordinatewise counterexample existence. Ask for an exact witness only when the application can use it. Do not combine separate coordinate witnesses into a simultaneous state.
6. Compare total service cost on representative inputs, including JSON/array conversion, construction, fallback and any requested witnesses. Use cached exact bounds for many policies on the same unchanged mass box.
7. Run the independent tests and near-boundary fixtures on the deployment environment. Keep the exact backend available as a reference.

No model training, GPU, dynamic tuning or threshold tolerance is necessary. The application threshold is the policy itself; the numerical allowance is computed from arithmetic precision and row count rather than learned.