Industry solution
Call quality control for sales teams
Quality control in sales almost always runs into the sample: the QA team manages to listen to 2–3% of calls, while what happens on the other 97% and why deals fall through stays out of view. The platform checks 100% of a sales team's calls automatically: it transcribes the conversation, scores it against your quality criteria, tags objections and risky moments, and alerts on problem records.
For sales this turns QA from a spot check into continuous control that affects revenue. Scoring on every call is uniform and objective, disputed conversations surface through semantic search, and the copilot assembles the needed sample from a plain query. The manager sees not random examples but the full quality picture by rep, stage and refusal reason — and knows exactly where the money is lost.
Which industry pains this solves
Leads go cold on slow callbacks
By the time a rep reaches the request, the lead has already talked to a competitor. Speed of the first touch drives conversion, but there's physically no one to call everyone back at once.
Call control is a 2–3% sample
A manager or the QA team gets to listen to 2–3% of calls. What reps actually say on the other 97% and why deals fall through 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
Full scoring instead of a sample before a comp review
Before revising the incentive plan, the manager needs to understand the team's real quality of work, not scores from a dozen manually listened calls. They define the quality criteria — contact established, need discovered, value pitched, objection handled, next step booked — and the platform scores each call over the period against them automatically.
The dashboard shows the score distribution by rep and stage: whose need-discovery consistently sags, who doesn't book the next step, where deals are most often lost. Low-scoring conversations with risky moments are auto-tagged, critical ones trigger alerts, and the manager finds edge cases with semantic search — "show calls where the client was ready to buy but the rep didn't close."
In the end the comp review rests on a full, uniform scoring of every call, not on a sample and subjective impression. Reps see that every conversation is scored, not a random one — and the mere fact of full coverage 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.
Related scenarios for this industry
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