Build an AI Receptionist With Whisper, Claude, Chatterbox and LiveKit: Architecture, Latency Budget and Starter Code

Most "AI receptionist" tutorials stop at a chatbot that talks. A receptionist a dental clinic will actually pay for has to pick up a real phone line, understand messy speech, follow the clinic's rules, book into a real calendar and do all of it fast enough that the caller never notices a gap.

This is the architecture we use as a starting point, built from four pieces you can run yourself:

Patient Prism's analysis of 11.5 million calls to 8,280 dental locations found 31% of callers hung up before reaching anyone ( report ). Only 42% of consumers say they leave a voicemail ( CallRail survey ). Missed calls are lost bookings, which makes "answer every call" an easy thing to sell.

The business math people quote is 100 clinics × $300 a month = $30,000 MRR. The engineering below is what makes that number survivable.

Callers start feeling awkward somewhere past a second of silence. Rough budget from "caller stops talking" to "agent audio starts":

These are targets to measure against, not guarantees. Measure each stage per call and log it. The biggest wins usually come from shorter prompts, streaming TTS by sentence and keeping the GPU workers warm.

LiveKit's starter template ( lk agent init my-agent --template agent-starter-python ) gives you a working agent with hosted models. The API evolves quickly, so treat the snippet below as the shape of the code and check the current voice AI quickstart for exact signatures.

The models are commodities now. The value is in the boring parts: integrations with each niche's software, reliable transfers, and reporting that shows the owner how many calls were saved. Pick one niche, nail those three, and the $30K MRR math stops being a slide.

Axionry is an AI product development studio that builds production voice agents. See the AI receptionist build, our voice AI development service, or price your setup with the voice AI cost calculator at axionry.com.