AI/ML, Automation

MBR AI Receptionist

Built a sophisticated AI receptionist with multi-tree routing logic to qualify new leads, handle vendor inquiries, and automate SMS/email follow-ups via N8N and Twilio.

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MBR AI Receptionist
Tech Stack

Technologies Used

Tools and platforms used to build this solution.

N8N
Twilio
Webhooks
AI Voice Agent
Process

How It Works

A closer look at the workflow behind the results.

1

Inbound Contact Received

A call, SMS, or vendor inquiry reaches the AI receptionist, triggering the multi-tree routing logic in N8N.

2

Lead Qualification & Routing

The system classifies the contact as a lead, vendor, or existing customer and routes it down the matching decision tree.

3

Automated Follow-Up

Automated SMS and email follow-ups go out based on the contact type, so every lead and vendor gets a timely response.

Metrics

Results & Impact

Measurable outcomes that demonstrate the value delivered.

Qualified 2,000+ leads monthly through intelligent multi-tree routing
Automated 1,500+ SMS and email follow-ups, reducing manual administrative effort by 70%
Handled vendor inquiries with 95% accuracy using AI-driven decision logic
Improved response times and customer engagement by 50% via automated workflows
Case Study

The Full Story

Case study snapshot

  • Client type: a small business front office that receives leads, vendor inquiries and calls from existing customers
  • Service: AI receptionist with multi-tree routing that qualifies leads, handles vendor inquiries and sends follow-ups
  • Stack: n8n, Twilio, webhooks, AI voice agent
  • Headline results: 2,000+ leads qualified every month, 1,500+ automated follow-ups, 70% less manual administrative effort

The problem

Every missed call or slow reply is a lead that may go to a competitor, but a full-time receptionist to triage every inquiry is not always practical. This front office also did not only receive customers. Vendors and existing customers contacted the same number, and each type needed a different response.

What we built

We built an AI receptionist with multi-tree routing logic in n8n. Every inbound call, SMS or vendor inquiry triggers the workflow, which decides what kind of contact it is and sends it down the matching decision tree:

  1. Inbound contact received: a call, text or vendor inquiry reaches the AI receptionist and starts the routing logic.
  2. Qualification and routing: the system classifies the contact as a new lead, a vendor or an existing customer and follows the right tree for that type.
  3. Automated follow-up: SMS and email follow-ups go out through Twilio based on the contact type, so each lead and vendor gets a timely response.

Measured results

  • 2,000+ leads qualified every month through multi-tree routing
  • 1,500+ SMS and email follow-ups automated, cutting manual administrative effort by 70%
  • 95% accuracy on vendor inquiries using AI-driven decision logic
  • 50% improvement in response times and customer engagement from automated workflows

Why it worked

Routing by contact type is what made the difference. A single script treats every caller the same. Separate trees let a vendor get a vendor answer and a lead get qualification questions and a quick follow-up, while staff only see the conversations that need a person.

Read more

For pricing and what to expect when you add an AI receptionist, see How Much Does an AI Receptionist Cost in 2026? and our AI receptionist page.

Why It Matters

Business Impact

For small businesses and service teams, every missed call or slow follow-up is a potential lead lost to a competitor, yet staffing a full-time receptionist to triage every inquiry is not always practical. By automatically qualifying leads, routing vendor inquiries, and sending timely SMS and email follow-ups, this system gives front-office operations the responsiveness of a larger team without the added payroll. Teams gain consistent, round-the-clock intake handling and can focus their attention on conversations that actually need a human touch.

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