Support quality, written down.

Chat assurance is QA for chat conversations. What the term means, why chat is not voice, the criteria worth scoring, and where it fits in a QA program.

The DSAT formula with a worked example, what counts as a negative rating, and why dissatisfaction is not simply the inverse of your CSAT score.

A fixed coaching cadence and a variable supply of real problems forces leads to invent feedback. Five better uses of the slot, and how to tell the difference.

A spreadsheet is the right place to start a QA programme and the wrong place to keep it. The five failure signatures, and what to carry across when you move.

Under peak pressure most teams loosen the standard, which corrupts the data for good. Cut coverage instead, and know which criteria can never move.

Deflection rate counts the tickets your AI agent closed, not the ones it closed well. How to audit the deflected population and find the silent failures.

Most support agents resent AI QA scoring. A minority prefer it. What separates them is traceable deductions, published criteria and an appeal that works.

Why support teams sampled conversations for QA, what a small sample can and cannot tell you, and why full coverage is replacing it for agent decisions.

What customer service QA involves in practice, how it differs from CSAT, and how review findings become coaching that makes service more consistent.

What belongs in a golden set, how to build one that does not flatter your grader, and how it underpins precision and recall for automated QA.

How to design a QA monitoring rhythm around full coverage instead of sampling, and what a review schedule cannot show you about agent quality.

A taxonomy of the AI agent errors your dashboard records as wins, with the signals that expose each one before customers stop trusting you.