Case studies

The numbers before us, and the numbers after us.

We baseline every engagement before a single AI employee goes live — then report the same metrics back month after month. Here is what that looks like in practice.

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0%Of inbound calls answered after go-live
0 secTypical first response to a new enquiry
0.4×More qualified strategy calls, same ad spend
0%Average reduction in admin hours
Proof

Real businesses. Measurable before and after.

Filter by the industry, measurement window and outcome you care about — then expand any metric to see exactly how it was calculated.

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Showing 3 of 3 case studies, sorted by most relevant

Professional Services·Cyprus & UK

3.4× more qualified strategy calls from the same ad spend

Paid traffic converted into form fills, but follow-up depended on one salesperson. Most leads were contacted the next day, and unqualified calls clogged the diary.

What we deployed: AI Sales Agent qualifying every inbound lead against an ICP scorecard within seconds, booking only qualified prospects, and running a 14-day nurture on the rest.

  • AI Sales Agent
  • AI Website Chat
  • AI Follow-up Agent
Before → After120 days after go-live

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Same budget, same team — the difference is that nothing waits until tomorrow anymore.
Managing Partner, B2B consulting firm
Property·Gauteng, SA

From 4 in 10 calls missed to every enquiry answered in 8 seconds

Agents were in viewings all day, so after-hours and overflow calls went to voicemail. Portal leads sat unanswered for hours and were lost to faster competitors.

What we deployed: An AI Receptionist answering every call and portal enquiry, qualifying budget and area, then booking viewings straight into agent calendars with WhatsApp reminders.

  • AI Receptionist
  • AI Appointment Setter
  • AI Follow-up Agent
Before → After90 days after go-live

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We stopped losing weekend enquiries overnight. The AI books the viewing before the buyer has time to phone anyone else.
Sales Director, Residential sales & rentals group
Medical·Western Cape, SA

Reception freed from the phone — and no-shows cut by more than half

Two receptionists spent most of the day on booking calls, patients waited on hold, and no-shows were quietly costing the practice a full diary slot every day.

What we deployed: AI Receptionist handling bookings, reschedules and medical-aid FAQs 24/7, plus an automated confirmation and reminder sequence over WhatsApp and SMS.

  • AI Receptionist
  • AI Customer Support
  • AI Follow-up Agent
Before → After6 months after go-live

Tap any metric to see baseline dates, data sources and the calculation.

Our front desk finally looks after the people in the room instead of the phone that never stops.
Practice Manager, Multi-doctor private practice
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Send us the same metrics for your business and we'll benchmark them against the closest comparable engagement. No obligation, no sales pitch, no long-term contracts.

  • Measured on revenue, not vanity dashboards
  • Month-to-month — no long-term contracts
  • Built and run in-house by the team that scopes it

Client names withheld at their request. Figures are drawn from each client's own reporting over the stated period; results vary by industry, lead volume and offer.

Methodology

How every number on this page was measured.

Results are only useful if you can interrogate them. Here is exactly how we baseline, measure and verify each before-and-after figure — and precisely what each metric means.

1. Baseline period

Before any AI employee goes live we record 30 days of unmodified activity — every inbound call, form, WhatsApp and DM. No process changes, no ad changes. That window becomes the 'before' figure.

2. Single source of truth

Metrics are pulled from systems the client already owns: the CRM (GoHighLevel / HubSpot), the telephony provider's call logs, calendar bookings and ad platform spend — never from our own dashboards alone.

3. Matched comparison window

The 'after' figure is the same length of time as the baseline, measured 30 days post go-live so onboarding noise is excluded. We compare like-for-like periods and flag seasonality where it exists.

4. Controlled variables

Ad budget, offer, pricing and headcount are held steady across both windows wherever possible. If a client changes spend or launches a new offer mid-measurement, we note it rather than claim the lift.

5. Ongoing reporting

The same metric definitions are reported monthly for the life of the engagement. Numbers on this page are the most recent full month, not a best-ever peak.

6. Client verification

Every published result is reviewed and signed off by the client before it appears here. Names are withheld where the client requested confidentiality, but the underlying reports are available on request under NDA.

Metric definitions

The same definitions are used across every engagement, so results are comparable client to client.

Calls answered
Inbound calls connected to a human or AI employee within 60 seconds, divided by total inbound calls. Abandoned-before-ring calls are excluded.
First response time
Median time between an enquiry arriving (call, form, WhatsApp, DM) and the first substantive reply — not an auto-acknowledgement.
Qualified strategy calls
Booked meetings that met the client's own qualification criteria and were attended. No-shows are removed from the count.
No-show rate
Confirmed bookings that did not attend, divided by total confirmed bookings in the period.
Cost per lead
Total paid media spend in the period divided by qualified leads, using the client's ad account figures — not our estimates.
Admin hours saved
Time-logged or role-estimated hours on tasks now handled end-to-end by an AI employee, confirmed with the staff who previously did the work.

What we don't claim: results vary by industry, offer strength, lead volume and how quickly a team adopts the system. These are real outcomes from real engagements, not a guarantee of your outcome. We'll happily walk you through the raw reports on a strategy call.

Your baseline

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