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Automating call quality assurance means moving your supervisor’s checklist into AI rules that score not a 2–3% sample but every single conversation. The order is this: first agree on what counts as a good call, then turn that into checkable criteria, run those criteria as auto-scoring across the whole flow, surface the results as alerts and dashboards, and finally calibrate the tagging with manual reviews. The human doesn’t go anywhere in the process — they stop listening to everything blind and focus on disputed cases and coaching instead. Below are the five steps of that shift.
Step 1. Agree on what a “good call” is
Automation won’t invent the criteria for you — it applies them. So the first step is purely human: get your managers and best operators to draw up a short list of what a quality conversation must contain. Usually that’s contact established, need discovered, mandatory phrases (introduced themselves, named the company, stated the terms), objections handled, and a next step booked.
The key requirement for a criterion is that it’s checkable. “The rep was polite” won’t do — two supervisors will score it differently. “The rep named themselves and the company in the first 20 seconds” or “an agreement on the next contact was recorded at the end of the call” will do, because the tagging comes out unambiguous. Start with five to seven such items that genuinely affect the outcome, not with a perfect thirty-line scorecard: a long checklist is one you’ll never calibrate and will abandon.
Step 2. Put the checklist into AI rules
Once the criteria are stated, translate them into automation rules. Each new call is first transcribed and split by speaker, then run through your checks. Some of them lean on metrics the platform computes itself — silence share, speech pace, interruptions, response delay, longest monologue, tone and sentiment; that’s a ready-made “conversation dynamics” layer, covered in more detail on the automatic call analysis page.
The semantic items on the checklist are handled by AI actions. A semantic tag (tagLlm) applies a label like “complaint” or “price objection” when it actually came up in the conversation, not just because a word matched. Extraction (extract) pulls structured fields from the transcript — whether the mandatory phrase was said, whether a next step was booked, which product was discussed. A free-form prompt (llmText) produces a short summary or a score against a criterion you describe. All nine automation actions and the condition logic are configured in the app itself; how recognition and tagging feed these rules is covered on the automatic call analysis page. To make the AI understand your specifics — products, scripts, mandatory phrases — attach reference documents to the rules: they act as context during scoring.
One important detail: new rules are created disabled. That lets you first test them against the archive and confirm the tagging matches your judgment, and only then switch them on for the live flow.
Step 3. Run auto-scoring across 100% of calls
Now enable the rules — and quality control stops being selective. Where a supervisor used to manage a few percent of conversations, auto-scoring runs across every one: both new calls in real time and the whole archive via a retro run. This is the key difference from manual QA — you measure not lucky examples but the entire flow, uniformly.
Start with a retro run over accumulated recordings: it immediately shows the baseline — how often the script is followed, where a particular stage most often fails, whose need-discovery consistently sags. Those numbers become the reference point against which you’ll measure the effect of changes. Full coverage on its own changes the team conversation: there’s no longer any “well, maybe you happened to catch a bad call” — the score covers all of them.
Step 4. Set up alerts and dashboards
A score is only useful when someone reacts to it, so add reactions to the rules. A notification (notify) emails the manager when a call breaks a critical item — say, the mandatory terms weren’t stated or the client is clearly unhappy. A signed webhook or a CRM note (crmPush) passes the scoring result to where the team already works, so the review doesn’t live a separate life in yet another tab. That way a problem call reaches the owner the same day instead of surfacing at a review a week later.
In parallel, build dashboards from the tagged data: the share of calls that passed the checklist, by operator and stage; script-compliance trends over time; sentiment trend; a before-and-after comparison around a change in coaching. Here you can see what’s invisible on individual calls: a topic thought to be rare turns out to top the complaints; one stage consistently fails across the whole shift, not one person; a spike in negativity coincided with a product change. This is exactly continuous quality control — the full scenario is in the quality control use case, and script adherence specifically in the script compliance use case.
Step 5. Calibrate the tagging with manual reviews
Automation doesn’t remove the human — it frees them for what the machine can’t do. Every so often, take a pool of disputed calls: the ones where the auto-score is on the boundary, where the operator disagrees, where a critical alert turned out to be false. Compare the auto-score with a manual one and refine the wording of the criteria until they’re unambiguous again. A good criterion is one where a person and a rule land on the same score; a mismatch almost always means the wording is vague, not that “the AI got it wrong.”
This calibration is what keeps the system alive. Scripts change, new products and objections appear, what mattered at the start loses relevance — the criteria have to follow. The best conversations, meanwhile, become a benchmark for training: they’re handy to show newcomers as a model, because they’re already tagged and were found by metrics, not by hand. That closes the loop: rules score the whole flow, people calibrate the rules and learn from the best examples, and quality measurably rises period over period.
What’s next
Automated quality control is also the foundation for more ambitious scenarios. As soon as you have clear criteria and continuous tagging of calls, the same loop can be pointed at coaching operators on specific failures, at real-time prompts, and, further out, at preparing data for a voice AI. The logical next step is to roll the tagged calls into clear dashboards and trends by operator, stage and period: that’s the advanced analytics page.