Industry solution
Clinic front-desk quality control across 100% of calls
Whether a patient books or hangs up and leaves for another clinic depends directly on the tone and competence of the front-desk conversation. But the senior administrator only manages to listen to 2–3% of calls, and rudeness, inattention or a missed booking get caught by chance — usually already through a patient complaint. The platform checks 100% of front-desk conversations: it transcribes every call, scores it against your criteria — politeness, completeness of the answer, whether a booking was offered, whether the doctor and service were named correctly — and flags problem dialogues instead of grabbing a couple of shifts for review.
For a clinic this turns quality control from a ritual into protection of revenue and reputation. Conversations with conflict, a refusal to book due to a long wait, or wrongly stated exam prep are auto-tagged by meaning, critical ones trigger alerts, and the manager assembles the needed sample with a single copilot query — for example, every call where the patient asked about a service but the administrator never offered to book. Scoring is applied to every administrator the same way, without different supervisors interpreting "politeness" their own way.
Which industry pains this solves
No-shows and empty slots
A patient forgot the appointment or changed their mind — and the doctor sits idle in a paid slot that someone from the waitlist could have taken.
The front desk is overloaded
At peak hours the line is busy, calls pile into a queue and drop. Admins are torn between the counter and the phone.
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 front-desk scoring before rolling out a communication standard
The clinic's manager is introducing a single communication standard at the front desk and wants to understand the real picture, not scores from a dozen manually listened calls. They define the quality criteria — greeted and introduced the clinic, identified the reason for the call, named the doctor and service correctly, offered a booking, handled a price or timing objection politely — and the platform scores each conversation over the period against them automatically.
The dashboard shows the distribution by administrator: whose offer-to-book consistently sags, who is curt with patients at peak hours, where a potential booking is most often lost. Dialogues with conflict and missed bookings are auto-tagged, rudeness triggers an alert, and the manager pulls edge cases with semantic search — "show calls where the patient was ready to book but never did." The team meeting reviews specific recordings with timecodes, not vague reproaches.
Two weeks later the dashboard shows the share of calls with an offered booking has risen and conflict calls have dropped. Quality control works across all administrators at once, and the communication standard finally lives in the conversation with the patient, not just in the rulebook on the front-desk wall.
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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