AI support quality grading: scoring every conversation

Most support quality reviews are theater. A lead spot-checks a handful of conversations a week, scores them on a rubric, and calls it QA. The other 95% of conversations go ungraded, which means the worst ones, the slow replies, the missed questions, the curt tone, mostly slip through. Automatic grading fixes the coverage problem.

Why manual QA does not scale

Reading conversations is slow, and it is the first thing that gets dropped when the inbox is busy. So QA ends up sampling a tiny, often unrepresentative slice. You learn that your best agent on a calm day does fine. You learn nothing about the 2am thread that pushed a merchant toward churn.

Grade every conversation, not a sample

The alternative is to score every conversation automatically. Convot’s AI quality grading reads each resolved conversation and rates the support, so you get coverage across the whole inbox instead of a hand-picked few. The conversations that score low are the ones worth your attention, surfaced for you instead of hidden in the pile.

Measure the things that actually matter

Good grading is not about politeness scores. It looks at whether the merchant’s actual question got answered, whether the response was timely, and whether the issue was resolved or just deflected. Those are the things that move your App Store rating and your churn, so those are the things to measure.

Turn scores into coaching

A grade is only useful if it changes behavior. Use low-scoring conversations as concrete coaching examples, not as a stick. “Here’s a thread where the question got missed, here’s what good looks like” teaches far better than an abstract rubric. Over time, the patterns in your low scores tell you what to fix systemically, a confusing feature, a missing help article, a gap in your canned responses.

Tie quality to revenue

The highest-leverage move is to weight quality by what is at stake. A mediocre reply to a free-tier tester is a minor issue. The same reply to a high-MRR merchant is a revenue risk. When grading and revenue data sit together, you know which quality misses actually cost you money. If you’re also using an AI agent to deflect tickets, What is an AI support agent? (And how to keep it from hallucinating) explains the grounding principles that make agent answers worth grading in the first place.

Support you measure is support you can improve. Automatic grading just makes “measure everything” finally possible for a small team.

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