Industry solution
Quality control of past-due calling across 100% of calls
Quality control in collection usually runs into the same 2–3% sample, but the stakes here are higher than in sales: a missed violation isn't a lost deal but a compliance incident and a complaint. The platform scores 100% of past-due calls automatically: it transcribes the conversation, checks it against your criteria — did the collector identify themselves, behave correctly, avoid threats and debt disclosure, capture a promise to pay — tags risky moments and alerts on problem records.
For a collection manager this makes QA continuous and uniform rather than a lottery. Scoring on every call runs on a single criterion, disputed conversations surface through semantic search, and the copilot assembles the needed sample from a plain query — for example, every call where a promise to pay was given but the tone was pushy. The manager sees not random examples but the full quality picture by collector, past-due bucket and outcome type — and understands where the standard of contact sags before a regulator or a customer notices.
Which industry pains this solves
Calling is expensive
Early-stage past-due work needs the same respectful call to hundreds of debtors. There aren't enough live agents for the whole volume, and every hour of their time is money.
Agents burn out
Monotonous reminders and tense conversations wear people down fast. Turnover rises, and with it both the quality and the tone of customer contact fall.
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
Full scoring of the calling stream before a collection audit
Before an internal collection audit, the manager needs to prove the standard of contact holds across the whole stream, not on a dozen manually listened calls. They define quality criteria tailored to past-due work: mandatory identification and call purpose, no threats or pressure, correct treatment of the debtor, no disclosure of the debt to third parties, a captured promise to pay. The platform assigns these scores to every call over the period automatically.
The dashboard shows the score distribution by collector and past-due bucket: whose tone correctness consistently sags on late past-due, who applies pressure more often, where PTP isn't captured. Calls with threats, debt disclosure and a harsh tone are auto-tagged, critical ones trigger alerts, and the manager finds edge cases with semantic search — "show calls where the debtor asked not to be called at work but the collector kept going."
In the end the audit rests on a full, uniform scoring of every conversation, and the manager has a compliance trail on any call rather than a sample and a subjective impression. Collectors see that every conversation is scored, not a random one — and the mere fact of full coverage keeps tone and discipline on the compliance script without extra manual 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.
Related scenarios for this industry
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