What it looks like
when automation
actually works.

The situation

A boutique med spa in was fielding 60–80 inbound calls per week — mostly appointment requests, pricing questions, and service inquiries. The front desk handled them well during business hours but anything after 5 PM or during peak treatment hours went to voicemail. The owner estimated that 30–40% of after-hours callers never called back.

The deployment

Magnex AI deployed an AI Receptionist connected to the spa's existing booking platform. The agent answered every call in the practice's voice, handled the top 12 FAQ categories without escalation, collected intake details for new client requests, and booked directly into the calendar. For calls requiring a specialist, it logged the conversation summary and scheduled a callback during business hours. The system went live in under two weeks with no changes to existing staff workflow.

What changed

After 30 days, after-hours bookings accounted for 28% of total new appointments — a category that had been effectively zero before. Front-desk staff reported fewer interruptions during treatments and more capacity to focus on clients who were physically present. The owner received a daily summary each morning showing call volume, booking rate, and any flagged conversations.

The situation

An independent real estate team in was running Facebook and Google ad campaigns generating 180–220 inbound leads per month. Their existing workflow relied on two agents manually calling back every lead within 24–48 hours. At that volume and response lag, the majority of leads had already spoken to a competing agent by the time anyone reached them. The team was spending more time on leads that never converted than on clients who were ready to act.

The deployment

Magnex AI deployed a Lead Qualifier and AI Receptionist in tandem. Every new lead from any ad platform triggered an immediate SMS and voice outreach within 90 seconds. The agent gathered timeline, budget, preferred neighborhoods, and whether the prospect was working with another agent — then scored the lead and routed accordingly. Hot leads were connected to a human agent within minutes. Warm leads entered a 14-day nurture sequence. Cold leads were logged and deprioritized automatically.

What changed

Within 60 days, the team's contact rate on new leads improved from 38% to 91% — because the AI was reaching prospects before they moved on. The number of leads that reached a qualified conversation tripled, while the agents' time shifted almost entirely to showing properties and closing. The Reporting Agent delivered a weekly pipeline summary that made team meetings 20 minutes shorter.

The situation

A Gwinnett County HVAC company had a customer list of 1,400 past clients — homeowners who had used the service at least once in the previous three years. The owner knew the list had value but his two technicians were too busy with active jobs to run any outreach. The list sat unused in a spreadsheet. Seasonal maintenance reminders were sent once a year by email with open rates around 12%.

The deployment

Magnex AI deployed the Sales Caller against the dormant list, prioritized by recency and service type. The agent reached out via SMS and voice, offered a seasonal maintenance check at a fixed rate, and booked directly into the technicians' calendar. Customers who did not respond entered a 3-touch follow-up sequence over 10 days. The AI Receptionist handled any inbound callbacks and routed them to booking immediately. The entire campaign ran autonomously over four weeks.

What changed

The four-week campaign generated 68 booked maintenance appointments at an average job value of $290, plus 14 system replacement consultations driven by the maintenance visits. Total attributed revenue exceeded $40,000 from a list that had been producing zero revenue. The owner's team worked from a full calendar with pre-qualified customers — no cold outreach required on their end.

The situation

A Buckhead med spa had a 3.9 Google rating with 47 total reviews — a mix of genuinely satisfied clients who never left a review, and a handful of negative posts that disproportionately dragged down the average. The owner knew her clients were happy but had no systematic way to ask. Staff sometimes mentioned reviews verbally at checkout, but the conversion rate on that approach was close to zero. Competitors in the same zip code held 4.6–4.8 ratings with 200+ reviews.

The deployment

Magnex AI deployed the Reputation Agent connected to the spa's booking system. After every completed appointment, the agent sent a personalized SMS at the optimal moment — typically 90 minutes post-service — thanking the client by name and service received, and including a single-tap review link. For clients who left 5-star reviews, the agent drafted a personalized response and published it within 4 hours. For reviews with 3 stars or below, the owner received an immediate alert with the full text and a drafted private response for review. Negative reviews were triaged within the hour instead of sitting unaddressed for weeks.

What changed

In 60 days, the spa received 61 new reviews with an average rating of 4.9. Overall Google rating moved from 3.9 to 4.7. When a negative review arrived — three in the 60-day window — the owner responded within the hour, which twice prompted the original reviewer to update their rating. The owner reported that new clients increasingly mentioned the reviews as the reason they chose the spa over competitors within a block.

The situation

A Marietta general contractor was generating solid inquiry volume — between 25 and 35 qualified leads per month — but converting fewer than one in five into signed contracts. The owner suspected the problem was in the proposal stage. After the site visit, he would manually put together a quote, usually 5–8 days later, and email it as a PDF. He had no visibility into whether the prospect opened it, and follow-up was inconsistent. By the time he followed up, many leads had already signed with a competitor who moved faster.

The deployment

Magnex AI deployed the Document Agent integrated with the contractor's intake notes and pricing templates. Within 4 hours of a completed site visit, the agent auto-generated a formatted proposal using the intake data, sent it for e-signature via DocuSign, and notified the owner. If the document was opened but not signed within 48 hours, the agent sent a warm follow-up SMS asking if the client had questions. If not signed by day 5, it escalated with a phone call script for the owner. All document events — sent, opened, signed, declined — were logged to the CRM automatically.

What changed

Average proposal turnaround dropped from 8 days to under 4 hours. The close rate on qualified leads improved from 19% to 34% — driven almost entirely by speed and consistent follow-up. The owner no longer spent weeknight hours writing quotes, and his CRM showed, for the first time, exactly where every lead was in the pipeline without needing to remember or check his inbox.