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AI receptionist case study: how a managed IT firm cut inbound admin by 87%

Downloads | AI agents, AI case studies, Operations and finance leaders

A regional managed IT provider built an AI receptionist for about £140 a month. It answers every call, day or night, captures the details and books an engineer slot for routine requests. Coordinators who spent around 15 hours a week on the phone now spend under 2 hours checking its work, and roughly a third more enquiries turn into booked jobs.

The firm has two engineering teams and handles 250+ support and onboarding requests a month. Before the agent, calls, emails and portal requests landed in different places, and anything sent overnight waited until morning.

The firm in numbers

  • 87% less coordinator time on inbound handling, from about 15 hours a week to under 2
  • £140 a month to run the stack: Zapier Pro, Twilio and the OpenAI API
  • 8 hours to build the first working version
  • About a third more enquiries converted into booked jobs
  • Paid for itself inside the first month, on the owner’s estimate

All figures are the firm’s own, as told to aibl.

How does an AI receptionist handle support calls?

It runs a scripted intake. The agent answers on a local number, asks for site, issue type, urgency and a preferred time, checks engineer availability in Google Calendar and books the slot. The caller gets a confirmation with a ticket reference. Anything outside the script, or any caller who asks for a person, goes to a coordinator.

What does it cost to run an AI receptionist?

About £140 a month for this firm: £47 for Zapier Pro, £35 to £40 for Twilio depending on call volume, and £20 to £30 for the OpenAI API. Upgrading to a better voice model added £10 a month. The founder says the saved coordinator hours and fewer missed calls covered the cost in the first month.

What went wrong with the first version?

It tried to do too much. The first build graded severity and matched response times on the call, but callers gave patchy information and coordinators redid the work. Cutting the job back to capture and book fixed that. A tinny voice model that misheard technical terms was the other early problem, solved with a better model and a confirmation message callers can correct.

How do you keep an AI receptionist GDPR compliant?

Design it in before the first live call. The firm learned this a few weeks in. Recordings are now kept for 30 days and access to them is restricted. Every call opens by saying AI is handling it, with a route to a person, and the privacy notices cover AI call handling.

Key takeaways

  • Give the agent one job, capture and book, and add triage later.
  • Send a confirmation so callers can correct what the agent captured.
  • Reconcile bookings against your job system every day.
  • Roll out to new enquiries first and keep a ‘press 1 for a person’ option.
  • Settle retention, disclosure and opt-out before launch, not after.

What’s in the full case study

If your team loses hours to phones and inboxes, the full case study shows how this firm built and ran its agent:

  • The exact stack, what each tool does and what it costs
  • The two fixes that made the first version usable
  • The daily reconciliation check that stops bookings going missing
  • How the firm introduced the agent without upsetting existing clients
  • The GDPR controls it added once calls went live
  • What the founder would do differently if starting again

This case study first appeared in aiblBRIEF’s AI in Practice series. Read the original story: A managed IT firm cut inbound admin time by 87% for £140 a month.

Put an AI receptionist on your phones

AI receptionist case study: how a managed IT firm cut inbound admin by 87%
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