With an AI voice agent handling 80-90% of routine calls, it's possible to cut phone support costs by 60% to 70% in the first month of operation. In the case we analysed — a dental clinic with two receptionists partly dedicated to phone support — the monthly cost of that function fell from $1,800 (two full-time receptionists) to $430 in staff + S/ 1,140 in Fluxr minutes in four weeks, with a one-time S/ 1,699 setup recovered before month two. Here's the full process, the numbers and the five mistakes that stop most companies reaching those savings.
The case: a dental clinic before the AI agent
The clinic has three dentists, sees 40 to 60 patients a day and receives around 500 calls a month. Before deploying the voice agent:
- Two full-time receptionists at $900 each = $1,800/month in direct salary allocated to the phone function
- Coverage: Monday to Friday, 8am to 7pm only
- Missed calls: roughly 28% (especially at lunchtime and at closing)
- No-shows: 18% of booked appointments
- Repetitive enquiries: 75% of calls were: hours, pricing, appointment confirmation or "do you accept X insurance?"
- No night or weekend coverage: patients left voicemails the team returned the next day, by which point many had already called another clinic
Before vs after the AI voice agent
| Indicator | Before | After (month 2) |
|---|---|---|
| Monthly phone support cost | $1,800 | S/ 1,140 (Fluxr) + $430 (staff) * |
| Hours covered | Mon-Fri 8am-7pm | 24/7 |
| Missed calls | ~28% | Under 5% |
| No-shows | 18% | 9% |
| Average wait time | 3.5 minutes | Under 10 seconds |
| Appointments booked per week | 120 | 148 (+23%) |
*Month-2 breakdown: S/ 1,140 in Fluxr minutes (approx. 1,500 min × S/ 0.76, VAT included) + $430 for 1 part-time receptionist covering in-person service and cases requiring human judgement. Fluxr minutes are billed in Peruvian soles; staff costs remain in dollars — the two components are not combined into a single figure.
The S/ 1,699 setup was recovered in week three of month one through the additional appointments the agent captured overnight — calls that were previously simply lost.
The 4-week process
Week 1 — Preparation and discovery session
The receptionists listed the 30 questions they received most often — they knew them by heart. Four main flows were defined: book a new appointment, reschedule, confirm or cancel, and questions about pricing and insurance. The scheduling system (Google Calendar) was connected so the agent could check availability in real time.
The discovery session with the Fluxr team took 30 minutes. With that information, the agent started being built.
What was not done: no attempt to perfect the knowledge base before launch. The principle is to start with enough and tune with real production data.
Week 2 — First version in production (48 hours after the session)
The agent went live with a base configuration: answering the 30 frequent questions, booking appointments against real availability, confirming and rescheduling existing appointments, and transferring to the human team for questions about specific treatments or cases with complex history.
The first 72 hours revealed three unanticipated situations: questions about specific insurance types, patients calling in English, and calls from suppliers. All three were configured as additional flows within that same period.
Week 3 — Tuning and data-driven optimisation
With two weeks of real transcripts:
- The questions the agent answered incompletely were identified and the knowledge base was updated
- The tone of voice was adjusted (patients preferred a warmer, less corporate tone)
- An automatic reminder was configured 24 hours before each appointment: "Your appointment is tomorrow at [time]. Can you confirm, or would you like to reschedule?"
No-shows fell from 18% to 11% in that week alone, purely from the automatic reminders. For more on that capability, see the voice agents page.
Week 4 — Rebalancing the human team
With the agent handling 82% of inbound calls, the distribution of work changed:
- One receptionist moved full-time to in-person service and personalised follow-up with patients undergoing treatment (work previously neglected because of call volume)
- The second receptionist moved to part-time, because the complex calls that do reach a human don't justify full-time dedication
- Night coverage: zero additional cost — the agent had been working 24/7 since week two
The 5 mistakes that prevent reaching 70% savings
Mistake 1: trying to automate everything from day one
Wanting the agent to handle 100% of cases at launch produces complex configurations that break on real questions. The right approach is to launch with the most repetitive flows and add complexity week by week, guided by real data.
Mistake 2: not reviewing transcripts in the first two weeks
Transcripts are the map of what the agent doesn't understand. If you don't review them in the first 14 days, problems accumulate silently, the human-transfer rate rises and customers get incorrect answers with nobody knowing.
Mistake 3: keeping the same human team without reassigning work
If the agent handles 80% of calls but the team keeps doing the same tasks in the same number of hours, the only visible saving is the AI's cost. The real saving — the one that reaches 60-70% — comes when people redirect their time to higher-value work the system can't do.
Mistake 4: launching without testing the handover to a human
A well-implemented handover — carrying the full conversation context — is what stops a customer having to repeat everything when they reach a human. If the agent simply hangs up or dumps to voicemail with no context, the experience fails at exactly the most critical moment. Use the ROI calculator to estimate whether the transfer volume justifies a full-time or part-time human agent.
Mistake 5: not configuring automatic appointment reminders
The appointment reminder is one of the agent's highest-ROI functions — it reduces no-shows directly, needs no extra human work, and runs at 3am with the same result as at 10am. Many teams leave it for "the next phase" and that phase never arrives. Configure it in week one.
When savings may be smaller
Honesty matters: if most of your calls require complex enquiries, extensive customer history or decisions depending on professional judgement — specialist medicine, law, corporate finance — the share of calls the agent can resolve alone falls, and with it the potential saving. In those cases the agent works better as a first filter and scheduling system, achieving reductions of 30-40% rather than 70%.
It's also worth noting that savings vary with volume: below 200 calls a month, the S/ 1,699 setup takes longer to recover. The ROI calculator lets you enter your real volume and see the exact estimate for your case before deciding.
For more savings examples by sector, visit our case studies section.
Ready to see your business's numbers?
- Use the ROI calculator — enter your call volume and your team's current cost for a personalised estimate.
- Request a custom quote — we review your case and tell you the same day how much you could save in the first 90 days.
- Compare the plans across voice, text and automations.
- Message us on WhatsApp — if you'd rather talk directly, we reply the same day.
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