A smarter new platform for field service calls
Where every field service call becomes instant, automated action.
CORE Approach
Protocall is an AI voice agent designed to automate how field service businesses receive, process, and act on inbound calls. It connects customers with the right service team through a real-time pipeline that handles identification, routing, and work order creation — all in one call.
The Core Intent of Our Approach
Our approach focused on eliminating the manual intake burden while ensuring accuracy, reliability, and complete visibility across customer resolution, service routing, and work order management.
Automate call intake
Replace manual dispatcher workflows with a real-time AI voice agent that handles every inbound call from first ring to completed work order.
Transform service routing
Replace inconsistent manual dispatch with structured, AI-driven classification and routing flows that reach the right team every time.
Preserve data ownership
Maintain complete control over customer records, call transcripts, and work order history — all within your own Azure infrastructure.
Build scalable architecture
Enable platform growth with a modular, containerised system on Azure designed to handle expanding call volumes without degradation.
THE PROBLEM
Field service call handling is often manual, error-prone, and inconsistent — creating costly delays for both dispatchers and customers waiting on service.
Manual call handling
Intake, customer lookup, and work order creation rely heavily on dispatchers — slow, inconsistent, and error-prone.
Broken customer resolution
Dispatchers struggle to identify callers accurately from partial addresses across disconnected data systems.
Lack of routing accuracy
Limited intelligence into service type, availability, and coverage zones leads to frequent wrong dispatches.
Scaling limitations
Existing manual systems fail to handle growing call volumes without adding headcount and operational cost.
WORKFLOW
A structured, step-by-step approach that transformed fragmented manual intake into a real-time, reliable, and fully automated pipeline.
Identify
gaps
We analysed broken intake flows, manual lookup processes, and error-prone dispatch across field service operations.
Design
structure
We built a custom AI pipeline connecting voice capture, customer resolution, and service classification in one flow.
Enable
execution
The system handles live calls, resolves customers, creates work orders, and escalates to agents without manual steps.
Deliver
visibility
Full call recording, structured logs, and performance dashboards give complete transparency across every interaction.
TECH INSIGHTS
The technology foundation that enables real-time voice handling, accurate customer resolution, and scalable automated operations across field service businesses.
Voice
pipeline
Real-time speech-to-text via Deepgram and neural text-to-speech via ElevenLabs — streamed with sub-200ms latency and no perceptible delay.
Customer
resolution
Structured fuzzy address matching using PostgreSQL pg_trgm, ranked by recency and coverage zone for reliable matches even on partial inputs.
AI
orchestration
A custom Python engine controls every conversation node — intent classification, routing logic, and work order creation — with zero platform dependency.
SIP
escalation
Live three-way call bridging between customer, AI agent, and human dispatcher — with full context narrated at handoff and zero transfer fees.
Modular
integrations
Clean REST API connections to field service management systems, work order platforms, and scheduling tools across any service operation.
Cloud
deployment
Containerised services on Azure Container Apps with automated CI/CD pipelines, blue/green deployments, and SIPp-validated load performance.
Result
Protocall eliminated manual call intake and routing while enabling accurate, automated operations for field service businesses.
Faster call handling
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Operational clarity
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Scalable platform
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Zero vendor lock-in
CASE STUDIES
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