Industry solution
Contact quality control for online stores
For an online store support quality is directly reviews, repeat purchases and returns. But a supervisor physically manages to listen to 2–3% of contacts, and how an agent handled a return dispute, a defect or a delivery delay on the other 97% stays out of view — surfacing later as an angry review on a marketplace. The platform checks 100% of a store's contacts the same way: it automatically turns both calls and chat/email threads into text and scores them against your quality criteria.
For a store this makes control continuous exactly where money and reputation are lost. Every conversation about a return, cancellation, defect or delay is scored — whether the return script was followed, whether the agent stayed polite during a conflict, whether a banned phrase slipped out. Problem dialogues are auto-tagged, critical ones trigger an alert, and the copilot assembles a slice from a plain query — "show every defect contact where the agent was rude." The manager sees not random examples but the full picture by agent and topic.
Which industry pains this solves
Abandoned carts go cold
The shopper reached checkout and left. Dialing everyone who didn't finish by hand is impossible — and the shopper never reads the email sequence.
"Where's my order" eats up support
Half of all contacts are about status, timelines and tracking numbers. Agents answer the same thing over and over instead of handling real problems.
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 review of returns and complaint handling
After a spike in negative reviews about support, the manager wants to understand how agents actually handle returns and complaints, not judge from a dozen selectively listened calls. They set e-commerce quality criteria: was the return policy followed, was an alternative offered instead of a refusal, was the agent polite during a defect conflict, did any banned phrases come up. The platform scores each contact over the period — both calls and chats — automatically.
Problem dialogues get auto-tags: "return-script violation," "rudeness," "banned phrase," and critical conflicts trigger a notification right after the conversation, before the shopper posts to a marketplace. Edge cases the manager pulls with semantic search — for example, every contact where the shopper asked for a size exchange and the agent processed a refund instead of saving the sale.
The dashboard shows the distribution by agent and topic: whose return-objection handling consistently sags, which product categories drive the most conflicts, where saving the customer most often falls through. Team-meeting reviews run on specific recordings with timecodes, not vague reproaches, and the mere fact that every contact is scored, not a random one, tightens agent 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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