AI automatically analyzes 100% of your calls, catches address mistakes that break deliveries, flags courier complaints before they turn into public reviews, and tracks upsell on every order — broken down by operator.
Avg. score
8.4
↑ +0.5
Upsell
34%
↑ +9%
Lost
7
↓ −7
Duration
2:48
↓ −22s
Operators on shift
AI insight: Operator Dan K. skips confirming the apartment and floor in 41% of calls. That's 6 failed deliveries and 2 refunds this week alone.
The hidden cost of every call
When every dollar of revenue comes through the phone line, one operator mistake or one minute in the queue costs you real money right now. Here's what's actually happening on your lines.
A new customer waits on hold while an operator handles a 'where's my courier' complaint
→ The queue for new orders backs up, the caller hangs up, and the order goes to a competitor
Up to 24% of calls during evening rush are lost to queueing or hold time
An operator mishears the apartment number, floor, or gate code
→ The courier can't find the address, the delivery fails, and the customer demands a refund
≈1 in 30 orders has a critical address error
The operator never offers a drink, a sauce, or mentions the free-delivery threshold
→ Lost extra revenue on an order that was already happening anyway
Average upsell is $0.50–$1.50 per order
💡 At 400 calls a day, even a 5% address-error rate means 20 failed deliveries every single day — and dozens of refunds a month.
Each refund costs about $7 on average, plus reputation damage. 20 × $7 × 30 = $4,200/month in avoidable losses.
How it works
The recording is automatically forwarded via webhook from your call center or phone system
The conversation is converted to text with operator/customer role detection and partner-brand recognition
Checks address accuracy, upsell attempts, tone, and whether the call is a courier or quality complaint
Ready-made insights and a live operator ranking for the call center manager
What you get
AI scores every call against a checklist — greeting, reading the address back out loud, confirming the order contents, tone. It works the same whether you handle 50 calls a day or 5,000.
→ You see how every operator performs on every shift, not just a sample.
AI flags when an operator skipped confirming the apartment, floor, gate code, or landmark — before the courier gets stuck on-site and the customer calls back angry.
→ Fewer failed deliveries and fewer refund requests.
AI automatically tells a new order apart from a status check or complaint call, and flags negative tone for immediate escalation to a manager while the customer is still on the line.
→ Unhappy customers get resolved in minutes instead of leaving a bad review.
Compare drink, sauce, and free-delivery-threshold upsell across operators and across the different partner restaurant menus running through the same call center.
→ Grow the average order value without changing a single partner's menu.
Product
Every operator, every shift, every partner brand in one place. Clear for the shift supervisor, precise for the owner.
Top operator
Марія К.
9.1/10 today
Upsell rate
38%
+7% this week
Lost orders
5
–7 vs yesterday
Avg. duration
2:41
–9s vs baseline
Upsell leaders
Lost call reasons
Customer sentiment
72%
Positive
19%
Neutral
9%
Negative
AI insight of the day
On Fridays between 7–9pm, average hold time climbs to 94 seconds and 16% of callers hang up. Adding one more operator to that shift is recommended.
Integrations
Setup takes up to 15 minutes even across multiple lines and operator teams. No extra hardware, no downtime.
ROI calculator
Even +35 upsells/day
For your call center that's:
Case study
−27%
Failed deliveries
+19%
Upsell rate
−41%
Complaint response time
14 days
Rollout
“We were handling around 600 calls a day across 9 operators and there was no way to manually review even 5% of them. AI showed us that two operators were systematically skipping the apartment and floor confirmation — that turned out to be the source of half our failed deliveries. Three weeks after coaching them, 'courier can't find the address' complaints were down by almost half.”
Marcus Reilly
Call Center Manager, "QuickBite Delivery"
😔
Before
The call center manager only found out about problem operators from Google reviews or refund requests. The real cause of failed deliveries was invisible.
🤖
Process
AI analyzed 100% of a week's calls and found that just two of nine operators accounted for 54% of all address errors, mostly on the evening shift.
🚀
Result
After targeted coaching for those two operators, failed deliveries dropped 27% and average complaint escalation time fell 41%.
FAQ