AI & Automation
Auto QA
Definition
Auto QA is the use of AI to automatically evaluate and score support interactions for quality, letting teams review every conversation rather than a small manual sample.
What is auto QA?
Auto QA (short for automated quality assurance, and also called AI QA) is the practice of using AI to evaluate the quality of support interactions automatically. Traditional quality assurance relies on a reviewer reading through tickets by hand and scoring them against a checklist. Auto QA applies that scoring with software, so it can run across far more conversations than a person ever could, consistently and continuously.
The aim is not to replace human judgement but to scale it. By handling the routine scoring, auto QA lets a team see the quality of its support as a whole, not just the slice a manual review happens to catch.
Auto QA versus manual sampling
The limitation auto QA addresses is sampling. Manual QA is slow, so teams review only a small percentage of tickets (often just one or two in a hundred) and hope the sample is representative. Anything outside it goes unseen, which means a recurring problem can persist for weeks before a reviewer stumbles on an example.
Automated scoring removes that blind spot by covering every conversation. Instead of inferring quality from a sample, a team can measure it across everything, then direct human attention to the interactions the scores flag as worth a closer look. The human review does not disappear; it becomes better targeted.
How auto QA works
An auto QA system reads each interaction and scores it against defined criteria, such as whether the customer's issue was resolved, whether the tone was appropriate, and whether the information given was accurate. It leans on techniques such as sentiment analysis to gauge how a conversation felt, and increasingly on AI evaluation methods to judge the quality of responses more holistically.
Because a score is only as trustworthy as the model behind it, mature setups pay attention to the confidence score attached to each judgement, sending borderline or low-confidence cases to a human rather than acting on the number alone.
Applying auto QA well
Auto QA works best as a lens, not a verdict. Define criteria that reflect what good support actually looks like for your team, use the scores to spot trends and outliers, and keep a human in the loop for coaching and for anything consequential. Read alongside your own conversation records and analytics, automated scoring turns a mountain of interactions into a clear picture of where quality is strong and where it needs attention.
Why it matters
Example
A team handling 8,000 conversations a month could only ever manually review a few hundred. Auto QA scores all 8,000 against the same rubric (resolution, accuracy, tone) and flags the lowest-scoring handful for a human to look at closely, so nothing slips through unseen.
How Resolve247 helps
See your support quality with Resolve247
Resolve247 is not a dedicated QA tool, but it gives you the raw material to keep an eye on quality: every AIChatbot conversation is logged, and an analytics dashboard shows how questions are being answered and where customers still get stuck. That makes it easier to see what is working and where your content needs attention.
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Related terms
Frequently asked questions
What is auto QA in customer support?
Auto QA is the use of AI to automatically score support interactions for quality, so a team can review every conversation instead of a small manual sample. It scores against criteria like resolution, accuracy, and tone.
How is auto QA different from manual QA?
Manual QA has a reviewer read and score a small sample of tickets by hand; auto QA uses software to score across all of them consistently. Automated coverage means issues outside a sample are no longer missed, while humans focus on coaching and edge cases.
What does auto QA measure?
Typically whether the customer's issue was resolved, whether the information was accurate, and whether the tone was appropriate, the same things a manual scorecard covers. It often draws on sentiment analysis and AI evaluation to make those judgements.
Can automated QA scores be trusted on their own?
They are most reliable used as a lens rather than a final verdict: strong for spotting trends and outliers across every conversation, with a human reviewing borderline or consequential cases. Watching the confidence attached to each score helps decide when to involve a person.