Reference builds,
end to end.
Each example below shows the problem, the system we'd build and the operational change it produces — illustrative reference architectures, not reports of a specific named client.
What These Builds Have in Common
Every reference build below starts from the same place: a real bottleneck that was being handled manually — enquiries triaged by hand across three inboxes, a support queue full of the same repeat questions, a missed call that meant a missed job. None of them started from a template; each system was mapped around the specific channels, tools and qualifying criteria that business already had in place.
What changed afterward follows the same shape too — an AI agent takes the first response instantly, on whatever channel the enquiry actually arrived on, and hands off to a person only once a conversation genuinely needs one. The "Before/ After" breakdown on each build below shows exactly what that looks like in practice.
Sample Case Study
AI Lead Qualification System
Problem
Enquiries arrived from a website form, a property portal and WhatsApp. They were handled manually during office hours, so response times stretched into the next working day.
Solution
A single AI agent now receives every enquiry regardless of channel, asks the qualifying questions a sales team would ask, scores the lead and books qualified prospects straight into a viewing slot.
Outcome
Every enquiry gets an immediate, contextual reply. Qualified leads reach the calendar without a human touch.
Before
- Three unconnected inboxes
- Replies during office hours only
- Manual scoring by memory
After
- One agent across every channel
- Immediate first response, 24/7
- Consistent scoring criteria
Sample Case Study
Support Deflection & Order Assistant
Problem
Order status, returns and sizing questions made up most of the support queue, and abandoned checkouts had no recovery flow at all.
Solution
An AI agent trained on store policies and live order data answers routine questions instantly and hands off to a human the moment a conversation needs one, while abandoned carts trigger an automated recovery sequence.
Outcome
Repeat questions are resolved without a ticket, and abandoned checkouts get a real second chance to convert.
Before
- Support queue full of repeat questions
- No cart-recovery flow
- Manual order lookups
After
- Routine questions answered instantly
- Automated recovery sequence
- Escalation only when it matters
Sample Case Study
WhatsApp Reception & Dispatch Flow
Problem
Missed calls during jobs meant missed business, and quoting a job required a callback and a back-and-forth before anything was booked.
Solution
A WhatsApp AI receptionist captures job details on first contact, prices against a rate card and books the visit — with a missed-call trigger that texts the customer automatically.
Outcome
Fewer missed jobs, faster quoting, and a booked visit before the customer looks elsewhere.
Before
- Missed calls, missed jobs
- Manual quoting by phone
- No after-hours coverage
After
- Every missed call gets an instant reply
- Instant starting-price quotes
- Booking captured 24/7
Sample case studies. These describe reference architectures and the operational differences they produce — they do not represent specific named clients or guaranteed results.
Case Studies FAQs
Are these real, named clients?
No — these are reference architectures describing the problem, the system we'd build and the operational change it produces, not results attributed to a specific named client. We built them this way so you can evaluate the approach without us disclosing another business's private data.
How is a new reference build scoped for my business?
The same way as any engagement — a discovery call where we map your actual enquiry channels, qualifying criteria and existing tools, then design the system around that rather than adapting one of the builds above.
What do these three examples have in common?
Each one replaces a manual, single-channel process (a callback, a support ticket, an office-hours-only reply) with an AI agent that responds immediately and hands off to a human only when a conversation genuinely needs one — the pattern behind most of what we build, regardless of industry.
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