Retell AI for Field Service: After-Hours Capture, Dispatch & PM Automation

Retell AI is the voice AI platform we use most frequently for field service implementations. It handles the specific requirements of inbound service call capture better than general-purpose alternatives — sub-500ms response latency, natural conversation handling, and robust webhook integration for pushing structured job data to dispatch systems.

This post covers how Retell AI actually works in a field service context: the architecture, what you configure, what the caller experience looks like, and where the data goes.

Why Retell AI for Field Service

Field service inbound calls have specific characteristics that make platform choice matter:

• Emergency urgency assessment. The AI needs to distinguish "my heat is out and it's 15 degrees outside" from "I want a quote for a new system." Different urgency, different routing.
• Structured data capture. Service address, unit type, symptom description, contact info, preferred callback time — all need to land in a dispatch system as structured fields, not a transcription blob.
• Natural conversation under stress. Customers calling about a broken A/C in August or a burst pipe are not patient. The AI needs to handle rapid, sometimes chaotic conversations without falling apart.
• Reliable after-hours performance. The whole point is 24/7 coverage. Reliability isn't optional.

Retell AI handles all four well. Competitors like Vapi and Bland are viable alternatives — Vapi for high call volume where per-minute cost matters, Bland for simpler use cases — but Retell's combination of latency, reliability, and developer ergonomics makes it our default for field service.

The Architecture of a Retell AI Field Service System

A complete Retell AI implementation for field service has four components:

1. The Voice Agent

Configured in the Retell dashboard: the AI model (Claude Haiku or GPT-4o-mini for cost efficiency), the voice (Retell provides several natural-sounding options), the system prompt, and the conversation flow.

The system prompt is where field service customization lives. It defines: how the agent identifies itself, what information it collects (in what order), how it assesses urgency, what it does with different call types (routine scheduling vs. emergency vs. general inquiry), and how it ends the call.

A well-written field service system prompt results in conversations that sound like a knowledgeable dispatcher — not a voice tree.

2. The Webhook Layer

When a call ends, Retell fires a webhook with the structured data captured during the conversation: caller name, phone number, service address, issue description, urgency assessment, preferred callback time, and the full transcript.

The webhook handler (typically a Node.js function or Make.com scenario) receives this data and routes it: create a job ticket in the dispatch system, send an SMS to the on-call tech for emergencies, send a booking confirmation to the customer, log the lead in the CRM.

3. The Dispatch Integration

Where the captured job data lands. This varies by business: ServiceTitan, Jobber, Housecall Pro, or a custom Airtable dispatch base. The integration maps Retell's webhook output to the fields in whatever system you're using.

For after-hours emergencies: the webhook handler additionally sends a text to the on-call tech with the job details and a direct link to the ticket. The tech can accept, call the customer, and update the job status from their phone.

4. The Monitoring Layer

Retell provides call logs, transcripts, and basic analytics. For field service operators, the key metrics are: capture rate (calls where the AI successfully collected job info), escalation rate (calls routed to on-call), and average call duration (a quality proxy — very short calls suggest hang-ups).

Supplemental monitoring via Make.com or a custom script logs per-call outcomes and sends daily summaries to your operations manager.

Sample Conversation Flow

Here's what a typical inbound HVAC emergency call looks like with a Retell AI agent:

Total call time: 90-120 seconds. Structured job ticket created: automatically. On-call tech notified: within 15 seconds of call end.

Configuration Decisions That Matter

Urgency Classification

Your system prompt needs explicit urgency criteria. "No cooling in extreme heat" = emergency, page on-call tech. "Unusual noise but unit is running" = next-day callback. "Want to schedule an annual tune-up" = book during business hours. The AI will follow whatever criteria you define — be specific.

Off-Hours Booking

Should the AI book same-day appointments for emergencies, or only capture the lead and promise a callback? For most field service businesses, booking confirmation (even with a 2-4 hour window) reduces no-shows compared to a callback promise.

Escalation Threshold

What triggers a live transfer to your on-call tech versus a text notification? Define this explicitly. Most operators prefer a text notification (not a live transfer) for after-hours — the tech can review the ticket and call the customer back when ready.

Cost Structure

Retell AI pricing is per-minute of call time. At scale: approximately $0.05-0.08/minute depending on the AI model configured. A typical inbound service call (90-120 seconds) costs $0.08-0.16 per call in platform fees.

For a business handling 100 after-hours calls per month, Retell platform cost is $8-16/month. Implementation and integration cost is separate — that's the one-time configuration work. Ongoing monthly cost for a complete system is typically $400-1,200/month including platform, hosting, and monitoring.

Getting Started

A Retell AI implementation for field service takes 1-2 weeks to configure and test properly. The technical work is not the bottleneck — writing the right system prompt (which requires understanding your specific business operations) and building the right integrations are where the implementation time goes.

DIY is possible if you have a developer comfortable with webhooks and API integrations. Implementation by a specialist who's done it before is typically faster and produces better results — the system prompt and conversation flow design matters more than most people expect, and getting it wrong means poor capture rates.