Automate Your Dispatch with a Custom Voice AI Agent
Syntora builds custom voice AI dispatch solutions for logistics and field service SMBs. We replace manual phone calls with AI agents that schedule jobs and confirm details.
The complexity depends on call volume and integration points. A local plumbing company needing to connect to a standard CRM is a 3-week build. A regional logistics firm integrating with a proprietary ERP and handling 500 calls per day requires more complex state management.
We built an AI dispatcher for a 15-person HVAC company that was handling 80 daily calls manually. The system went live in 4 weeks. It now autonomously handles 90% of inbound scheduling calls, reducing dispatcher phone time by 7 hours per day.
What Problem Does This Solve?
Many field service businesses rely on pre-built Interactive Voice Response (IVR) systems from providers like Twilio. These are rigid, menu-driven systems. They work for simple routing but fail with natural language. A customer saying “My AC unit is making a grinding noise, can you send someone tomorrow morning?” breaks a flow designed for “Press 2 for service.” You end up building a complex tree that still dumps most callers to a human.
Off-the-shelf voicebots are the next step, but they are typically designed for simple FAQs, not complex scheduling that requires real-time checks against a technician's calendar. They fail when they need to query a third-party CRM with specific business logic, like confirming if a technician is certified for a particular appliance model. This lack of deep integration turns the voicebot into a simple message-taker.
The only other option is hiring more dispatchers, but that scales cost linearly with call volume and introduces training overhead. A new dispatcher can take 2-3 months to learn your technician specialties and service areas, leading to booking errors that cost you truck rolls and customer trust. The core problem is that generic tools cannot handle your specific operational logic.
How Does It Work?
We start with 20-30 of your existing call recordings. These are transcribed and analyzed using Anthropic's Claude 3 Sonnet to map out the 5-7 core customer intents, such as scheduling a new job, checking an existing appointment, or requesting a quote. This analysis forms the blueprint for the AI agent's conversational abilities and prompt engineering.
We build the core logic as a Python service using the FastAPI framework. This service manages the conversation state and uses the Claude API for intent recognition and entity extraction (address, service type, requested time). For real-time availability checks, we use `httpx` to make non-blocking API calls to your existing scheduling system or CRM, ensuring the agent only offers appointment slots that are actually open.
The entire service is deployed on AWS Lambda, which allows it to scale from one call to 50 concurrent calls without any manual intervention. This serverless architecture keeps hosting costs under $50 per month for a typical SMB's call volume. Conversation state and booking details are temporarily stored in a Supabase Postgres database, allowing the agent to resume a dropped call or handle follow-up questions with full context.
We integrate this backend with a telephony provider like Twilio to manage the voice stream. Audio is transcribed, processed by our FastAPI service, and the AI’s generated response is converted back to speech. The entire round-trip, from a customer finishing their sentence to the AI starting its reply, takes less than 300ms, creating a fluid and natural conversational experience.
What Are the Key Benefits?
Your AI Dispatcher is Live in 4 Weeks
From call analysis to handling live customer calls in 20 business days. This avoids the 3-6 month hiring and training cycle for a new human dispatcher.
Fixed Build Cost, Not Per-Call Pricing
One scoped project fee, then minimal monthly AWS hosting costs. No per-minute or per-call charges that penalize you for growing your business.
You Own the Code and the AI Prompts
We deliver the complete Python source code to your GitHub. You are not locked into our platform and can have any developer modify or extend the system.
Real-Time Alerts for Failed Bookings
The system uses `structlog` for structured logging. If the AI fails to book a job, it sends a Slack alert with the call transcript for human review.
Connects Directly to Your Scheduling System
We build custom API integrations to your existing CRM or scheduling software. The AI checks real-time availability in systems like ServiceTitan or Jobber.
What Does the Process Look Like?
Scoping & Call Analysis (Week 1)
You provide access to call recordings. We analyze conversation patterns and define the core scheduling logic and required API integrations. You receive a detailed technical spec.
Core AI Agent Build (Week 2)
We develop the conversation logic using the Claude API and build the FastAPI service. You receive a link to a staging environment to interact with the agent via text.
Integration & Telephony Setup (Week 3)
We connect the AI agent to your CRM and a telephony provider. We run end-to-end tests with live phone numbers. You receive a test number to call the agent yourself.
Launch & Monitoring (Week 4+)
We switch the system to your main business line. For the first 30 days, we monitor failed interactions, tune prompts, and provide a runbook for long-term maintenance.
Frequently Asked Questions
- How much does a custom voice AI dispatcher cost?
- The cost depends on the number of integrations and the complexity of your scheduling logic. A simple system connecting to one calendar API is a straightforward build. Integrating with a legacy ERP or handling multi-step scheduling requires more development. We provide a fixed-price quote after a 30-minute discovery call where we map out your exact needs.
- What happens if the AI can't understand a caller?
- If the AI fails to understand a user's intent after two attempts, it executes a fallback path. It will say, 'I'm having trouble understanding, let me connect you with an operator,' and transfer the call to your main office line. This ensures no customer is left in a frustrating loop. A transcript of the failed call is logged for review.
- How is this different from a service like Talkdesk?
- Talkdesk provides a contact center platform, but you still need engineers to build the custom AI logic on top of it. General-purpose chatbots are not designed for the specific state-tracking needs of dispatch. Syntora provides the end-to-end solution: the AI logic, the integrations, and the production deployment, built specifically for your workflow from day one.
- Does it work with different accents or languages?
- The underlying transcription and language models perform very well with most English accents (US, UK, Australian, Indian). For other languages, we would need to evaluate the model's performance on a case-by-case basis. We typically recommend starting with a single language and expanding once the core system is proven to be effective.
- How do you handle sensitive customer information?
- We do not store full call audio after processing. Personally identifiable information (PII) like names and addresses are passed directly to your CRM via its secure API and are only stored temporarily during the active call. The database is encrypted at rest and in transit. We can also implement PII redaction in logs if required for compliance.
- Can this system handle our busy season when call volume triples?
- Yes. The system is built on AWS Lambda, which scales automatically based on demand. It can handle 1 or 100 simultaneous calls without any changes to the architecture. Your monthly AWS bill will scale with usage, but you do not need to provision servers or worry about capacity planning. It is designed for variable workloads from the start.
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