Behind the Technology: Lessons from 2 Million Calls

The Latency Challenge

When we set out to build FrontDeskOS, we knew that latency would make or break the user experience. Human conversation tolerates pauses of about 200 milliseconds before they feel awkward, so every component in our voice pipeline — speech-to-text, intent classification, response generation, and text-to-speech — had to fit within that budget. Achieving this at scale across 2 million calls per month required rethinking conventional architectures from the ground up.

Edge Inference Architecture

Our first major decision was to run inference at the edge. Instead of routing audio to a central data center, we deploy lightweight models to points of presence within 50 milliseconds of the caller. The edge handles real-time transcription and intent classification, while heavier tasks like sentiment analysis and knowledge-base retrieval run asynchronously in our core infrastructure. This hybrid approach shaves 120 milliseconds off round-trip latency compared to a fully centralized design.

Developers can tap into this pipeline through our API and webhook system. Learn more about our team and the engineering philosophy behind these decisions.

Streaming Response Architecture

The second breakthrough came from our streaming response architecture. Traditional chatbot systems wait for a complete user utterance, process it, and then generate a full response — a pattern that adds hundreds of milliseconds of dead air. FrontDeskOS streams its response token by token, beginning text-to-speech synthesis while the language model is still generating. Combined with speculative prefetching of likely responses, this technique delivers responses that feel instantaneous to the caller. The engineering is complex, but the result is simple: every caller gets a human-quality conversation experience, every time.