SupportGrade

Small support teams increasingly mix human agents with automated responders, but sampled retrospective reviews cannot show whether both channels follow the same policy or where automated behavior is drifting. SupportGrade converts the company's approved scorecard into versioned evaluation criteria, runs them across imported conversations and places only configured high-risk automated replies into a pre-send review queue. The supplied research confirms a live conversation-QA vendor with public volume tiers, two named customers and an integration for conversational use. It also supports a Western small-business gap and reports no pre-send gate in the reviewed product, but geography and feature absence are not durable moats. A model score is an assessment candidate, not evidence that an agent violated policy. Customer message, conversation context, applicable policy version, criterion result, reviewer disposition, coaching action, pre-send approval, platform delivery acknowledgement, customer response and support outcome remain separate. Sensitive conversations require minimization, role-based access, retention and redaction. The product can expand review coverage and make drift visible. It cannot guarantee correct scoring, prevent all harmful replies, replace support leadership or prove customer outcomes from QA scores alone.

A small or mid-sized support leader operating both human and automated conversations with an owned scorecard and named quality reviewers.