How We Built an AI Voice Layer in 3 Weeks
Michael
10 Apr 2026
Three weeks. That is how long it took us to go from an initial brief to a live AI voice agent handling inbound logistics calls in three languages. Here is exactly how we did it, what broke, and what we learned.
The Brief: Replace Hold Music With Intelligence
Our client, a European logistics firm, was drowning in inbound calls. Drivers calling for delivery updates, warehouse staff asking about shipment statuses, customers chasing orders. Their team of eight call handlers was handling 400+ calls per day. Response times were brutal and mistakes costly. They needed something that could handle the volume without losing the human feel.
Week One: Architecture and Language Modelling
We spent the first week on architecture decisions that would define everything else. Choosing the right speech-to-text pipeline for Estonian, English, and Russian accents. Designing the intent classification layer. Building the integration with their TMS (transport management system) so the agent could actually answer questions with real data rather than generic responses.
- Speech-to-text: Whisper with custom fine-tuning for logistics terminology
- Intent classification: GPT-4o with a structured prompt and fallback routing
- TMS integration: REST API with caching to reduce latency
- Language detection: automatic, with mid-call switching support
Week Two: Voice Persona and Edge Cases
The hardest part of voice AI is not the technology — it is the human layer. We spent week two defining how the agent sounds, how it handles confusion, what it does when it cannot find an answer, and when it escalates to a human. We ran 200+ test calls internally, deliberately breaking it to map every failure mode.
Week Three: Deployment and Live Monitoring
We deployed on a Tuesday. By Friday, the agent was handling 68% of inbound calls without human intervention. We monitored every call, listened to edge cases, and shipped three quick fixes over the week. By the end of week three, call handler workload had dropped by more than half.
Voice AI is not a distant aspiration. With the right team and the right architecture, you can go from zero to live in weeks, not months. The key is building for your specific call patterns, your specific language needs, and your specific data sources — not deploying a generic solution and hoping it fits.
