Industry solution
Inquiry quality control for customer support
In support, quality control traditionally lives on a scorecard and a sample: a supervisor manually listens to a few conversations per agent per week and scores them against a checklist. The problem isn't only volume — 97% of inquiries go unchecked at all — but also inconsistency: two supervisors read "showed empathy" and "resolved the issue" differently, and the score depends on whose shift was pulled for review. The platform applies your quality scorecard to every inquiry the same way: it transcribes the conversation and scores greeting, customer identification, script adherence, tone, presence of forbidden phrases and a first-contact-resolution flag.
For a contact center this turns QA from a spot audit into a continuous, objective process tied to support's business metrics. FCR, the share of script violations and negative tone are computed across the whole flow, not on a listened-to dozen; the supervisor finds disputed dialogs with semantic search; and critical violations — rudeness, a promise that shouldn't be made, a forbidden phrase — don't sink in the stream but surface as an alert right after the conversation.
Which industry pains this solves
Queue and dropped calls
At peak and overnight customers can't get through: the call piles up in the queue, drops off and becomes a missed contact no one ever learns about.
Quality by gut feel
You only get to review 2–3% of conversations. Whether the script held, whether the agent was polite, whether it was solved on first contact — stays out of view.
How it works
A step-by-step scenario — the same steps as in the full use case, applied to your industry.
- 1
Bring the whole stream into the platform
Connect telephony and email or upload recordings and message threads — interactions land in one pipeline. The automation fires on every new conversation, while a retro run works through the archive you already have.
- 2
Auto-transcribe the entire stream
An automation rule transcribes every call with the engine you choose, and text interactions are imported as-is. Conversations become a single body of text ready for scoring — no manual transcription and no sampling.
- 3
Score against your quality criteria
AI actions check the conversation by your rules: was the script followed, was the agent polite, did prohibited phrases come up. The result is written into metrics (yes/no, a score, extracted wording) computed the same way on every interaction.
- 4
Tags and alerts on violations
Problem conversations are tagged automatically ("script violation", "rudeness", "stop phrase"), and critical ones trigger a notification or webhook. A violation does not sink into the stream — it surfaces on its own right after the conversation ends.
- 5
Per-agent dashboard
Quality metrics roll up into dashboards: script adherence rate, violation frequency and tone by agent, team and period. You can see who slips and where, and semantic search pulls up specific examples to review.
Industry scenario
Calibrating the scorecard before agent certification
Before quarterly certification the manager needs an honest quality picture across the whole team, not scores from a dozen manually listened calls. They digitize the existing support checklist as quality criteria: greeted and introduced themselves, identified the customer, stayed on script, was polite, used no forbidden phrases, resolved the issue on first contact. First the rule runs on a hundred conversations already scored by a supervisor — to verify that the AI score matches the manual one — and then it's switched on across 100% of the quarter's flow.
The quality dashboard shows the distribution by agent and team: whose empathy consistently sags, who slips on script more often, who has a low FCR — the customer comes back with the same question. Dialogs with violations are auto-tagged ("rudeness," "script violation," "forbidden phrase"); the supervisor got an alert on critical ones during the call itself; and for each agent's certification a set of their real problem and exemplary calls with timecodes is assembled.
In the end certification rests on a full, uniform scoring of every inquiry, not a random sample and a supervisor's subjective reading. The agent sees not "I was unlucky with the call that got listened to" but objective statistics across all their conversations — and the mere fact that every one is scored tightens script discipline without extra oversight.
Key facts
- Coverage
- 100% of conversations are scored — calls and messages — not a sample of the 2–3% someone reviewed.
- How it is scored
- AI checks script adherence, politeness and prohibited phrases by your criteria and writes the result into metrics.
- Response to violations
- Problem conversations are tagged automatically, and critical ones trigger a notification or webhook.
- Analytics
- Dashboards roll up quality by agent, team and period; semantic search retrieves examples to review.
- Where to start
- Connect telephony and email or upload the archive — one automation rule handles both the new stream and what you already have.
Ready to start?
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