Case studies

Real firms. Real fares. Back on the meter.

Ten private-hire operators, ten different problems, answered by an AI voice agent on the phones or a WhatsApp bot in the chat. Here is exactly what changed once each one went live.

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AI Voice BookingHigh-volume call centre

The firm that turned a third of missed calls into booked fares

The challenge

Six phone lines still dropped roughly one call in three at peak. Every engaged tone was a fare gone to a rival, and the office had no record it had even happened.

What we built

An AI voice agent answers the instant every human line is busy, takes the whole booking by voice on the firm's own tariff, and writes it into iCabbi. No caller hears an engaged tone again.

Calls left on an engaged tone

First ring

Overflow answered instantly

+1/3

Of lost calls recovered as fares

WhatsApp Chat BotAirport-transfer specialist

An airport operator that beats flights that never run on time

The challenge

Pre-booked airport runs fell apart when flights moved. Customers re-messaged to change times, and matching the right terminal to the right pickup ate the controllers' day.

What we built

An airport flow with live flight tracking and terminal-aware routing, LHR terminal zones mapped correctly, plus a manage-booking path so riders move their own pickups when a flight shifts.

Terminal-aware

Right terminal on every run

Self-serve

Riders adjust their own times

Fewer reruns

Less rework when a flight slips

AI Voice BookingRural & long-distance firm

An operator that closed its night desk without losing a fare

The challenge

Two overnight controllers cost a fortune to cover a handful of calls an hour, but cutting them risked the 4am airport and hospital runs that pay the best.

What we built

The voice agent took every overnight call on the firm's fares and dispatch, quoting and booking unattended. Controllers came back to a full overnight job list, not a voicemail box.

2 desks

Overnight controller shifts removed

24/7

Calls answered through the night

Payroll

Night-cover cost taken off the books

WhatsApp Chat BotNight-economy operator

A late-night firm that survives the 2am chucking-out rush

The challenge

Friday and Saturday after midnight, hundreds of people leaving bars hit the line at once. Most got an engaged tone; many were too loud or too merry to talk clearly anyway.

What we built

A bot that takes voice notes straight from the pavement and reads them through the noise, plus tap-to-book quick replies for anyone who would rather not type. The rush spreads across the chat instead of the phone.

Peak-proof

Hundreds of chats at once, no queue

Voice-first

Bookings taken from noisy streets

Steady wait

Reply time holds through the surge

AI Voice BookingEstablished local firm

A firm whose regulars still phone, and now always get through

The challenge

A loyal, older customer base books by phone and will not touch an app. At peak those exact regulars hit an engaged tone and rang the rank instead.

What we built

A natural British-voice agent that sounds like the office, picks up instantly and books the way the regulars expect, no menus and no 'press 1 for bookings'. The people who phone are the people who stay.

No menus

Natural speech, not a phone tree

Engaged tones for loyal regulars

Retained

Phone-first customers kept on the books

WhatsApp Chat BotSchool & account contracts

A contract firm that automated its standing bookings

The challenge

School runs, hospital appointments and account customers meant the same recurring jobs re-keyed every week, and a flood of small changes the office handled by hand.

What we built

Recognised account customers, recurring journeys booked and amended in chat, and changes logged against the right standing job. The team approves, the bot does the typing.

Recurring

Standing jobs booked in a tap

Account-aware

Known riders recognised on sight

Hands off

Routine changes handled in chat

AI Voice BookingGrowing private-hire firm

The operator that stopped quoting fares wrong on the phone

The challenge

New phone staff under-quoted long runs and argued the fare with drivers afterwards. Training a controller to price every job by heart took weeks the firm did not have.

What we built

The voice agent quotes from the firm's own tariff on every call, reads the fare back before it books, and saves a recording of the call so any dispute has the words on file.

Tariff-true

Every quote from the firm's own rates

Recorded

A transcript behind every booking

Day one

No weeks of controller pricing training

WhatsApp Chat BotAccessible & specialist vehicles

A firm that never sends the wrong vehicle to an accessible job

The challenge

Wheelchair-accessible and larger-vehicle requests slipped through on busy lines, and a saloon turning up to a wheelchair booking is a complaint and a lost customer.

What we built

The chatbot asks the vehicle question every time, captures access needs up front and holds the job against the right vehicle type before it reaches dispatch.

Vehicle-sure

Access needs captured every booking

No mismatch

Right vehicle type held from the start

On record

Requirements logged for the driver

AI Voice BookingTown private-hire fleet

A 60-car firm that stopped losing its after-hours work

The challenge

Evening and weekend demand spilled past what two phone lines could answer. Engaged tones meant lost fares, and the office had no idea how many rides were slipping to the rank or the firm next door.

What we built

An AI voice agent on their own fares and AutoCab dispatch, picking up every after-hours call, with the WhatsApp bot alongside it. ASAP, scheduled and managed bookings handled in one conversation, written straight into dispatch.

24/7

Booking coverage with no night staff

< 3s

From message to first reply

After-hours calls left ringing out

AI Voice BookingMulti-branch town firm

An operator that put its phone line and WhatsApp in one place

The challenge

Calls hit the office line while WhatsApp messages piled up unseen, each watched by a different person, and the same rider counted as two separate conversations.

What we built

One automation behind both the booking line and WhatsApp, with one customer record behind it. A caller who phones this week and messages on WhatsApp the next is the same person, with one history.

1 record

Phone and WhatsApp in one place

1 history

One thread per rider, not two

No relearn

Nothing new for staff to log into

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