Quality control
Quality control on 100% of conversations, not a sample
Manual review reaches 2–3% of messages, and violations in that sample are found by chance. The platform checks every interaction across any channel: it turns the whole stream into text, scores it against your quality criteria, flags problem conversations and shows the picture for each agent. QA stops being a lottery and becomes complete.
The problem
Classic quality control is capped by human capacity: a supervisor can realistically get through only a few percent of conversations. The other 97% no one listened to or read — rudeness, script deviations, a missed offer or a prohibited phrase simply never make it into the sample.
Even what is reviewed is judged unevenly: different supervisors read "politeness" and "following the rules" their own way, there is no single bar, and the outcome depends on whose shift and whose conversation got pulled for review. There is nothing solid to coach agents on or to settle scoring disputes with.
As a result, quality runs blind: a problem surfaces as a customer complaint or a lost deal rather than as a violation caught in time. You need a way to look at every interaction by the same rules, not to grab random ones.
How the platform solves it
- 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.
The result
Quality control becomes complete and objective: every interaction is checked by the same rules, violations find themselves, and decisions about agents rest on data rather than a random sample.
- 100% of conversations reviewed — both calls and messages — instead of a manual 2–3% sample.
- The same quality criteria apply to every interaction identically, without supervisor subjectivity.
- Violations are caught automatically: tags and alerts surface problem conversations right after they end.
- A per-agent dashboard shows where quality actually slips and gives concrete examples for coaching.
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.
FAQ
Frequently asked questions
Does this work for calls only, or messages too?
Both. Calls are transcribed automatically, and text interactions — chats, emails, tickets — are imported into the same pipeline. Quality criteria apply to any conversation the same way, so coverage is complete across every channel.
How do I set my own quality criteria?
Criteria are described in automation rules: AI actions check script adherence, politeness and the presence of prohibited phrases and write the result into metrics (yes/no, a score, extracted wording). You can calibrate the rules on real conversations before turning them on across the whole stream.
What happens when a violation is found?
The conversation is tagged automatically, and critical rules trigger an email notification or a signed webhook to an external system. That way problem conversations surface right after they end, instead of showing up later as a complaint.
Can I review the existing archive, not just new interactions?
Yes. An automation rule applies to every new conversation, and a retro run works through the archive you already have by the same criteria — so you get a complete picture of quality immediately, not only for future interactions.
How does this help with working on agents?
Quality metrics roll up into dashboards by agent, team and period: you see script adherence rate, violation frequency and tone. Semantic search retrieves specific examples of problem conversations that make coaching easy to ground in reality.
Product
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