Business Automation Example
A done-for-you AI receptionist for a multi-location service business
A US-based auto detailing service network with multiple locations (identity anonymized at the client's discretion).
Identity anonymized at the client's discretion. Every number below is real and verified. See our case study policy on the Case Studies page.
The challenge
Every missed call was a missed booking. Front-of-house staff were already stretched across in-person customers and the phone, so calls outside business hours, or during busy periods, went unanswered. Each location needed the same quality of phone coverage without adding headcount at every site.
What we built
Inbound and outbound agents, built separately
Rather than one generic script, we built distinct inbound (answering, booking, FAQs) and outbound (confirmations, reactivation, no-show follow-up) voice agents, each trained on that business's actual services and pricing.
A tested evaluation suite before go-live
Every agent configuration was run through a structured evaluation suite covering common call scenarios before it ever took a real call, so quality was verified before launch, not discovered afterward.
Demo templates, then per-client tuning
We maintain versioned demo templates for the auto detailing vertical specifically, then tune each live deployment to the individual business's booking flow, hours, and service menu.
The outcome
Calls now get answered at every location, around the clock, without adding front-of-house staff. Booking, rescheduling, and routine questions are handled by the voice agent, freeing staff to focus on customers already on site. We don't publish a specific percentage or dollar figure for this account because we haven't independently verified call-volume-recovered numbers with the client's own systems, unlike the SEO case study on this page, where every number is pulled directly from the client's analytics. We'd rather show accurate qualitative results than an invented statistic.
Frequently asked questions
How is this different from a generic AI phone system?
The agent is trained on the specific business's real services, pricing, and booking flow, built and tested through a structured evaluation process before it ever answers a real call, not a one-size-fits-all script.
Does this work across multiple business locations?
Yes. Each location gets its own tuned configuration built on a shared, tested template, so quality stays consistent without starting from scratch for every site.
What happens when the AI can't handle a call?
It hands off cleanly to a team member, takes a detailed message, or books a callback. Every conversation is logged, so nothing gets lost even when the AI reaches the edge of what it can handle.
Why doesn't this case study include specific numbers like the SEO one?
Because we hold ourselves to only publishing numbers we can verify. This account's outcome is real and positive, but we haven't yet pulled independently verified call-recovery data the way we have for our SEO case study. We'll update this page with real figures once they're confirmed.
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