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RizoReception: a local AI receptionist

Connecting telephony, speech and local inference while keeping call transfers behind a separate policy.

RizoReception: a local AI receptionist

The problem

A telephone conversation is a streaming interaction with awkward timing. Recognition, model latency and speech playback all share a caller’s patience budget.

The architecture

RizoReception connects a PBX audio path to speech recognition, a local language model and speech synthesis. Transfer policy is a separate boundary. A plausible sentence from a model is not authority to send a call anywhere it likes.

The hard part

A good response in a browser does not prove a good response over a telephone. Audio quality, pauses, interruptions and the actual transfer path all need acceptance evidence.

Documented milestones

Earlier work recorded human-call transfer acceptance. A later voice activation had its own automated checks and outstanding listening and transfer confirmation. Those records must remain distinct: a previous voice’s acceptance cannot qualify a replacement automatically.

Current limits

The project is presented as experimental. No caller data, private destinations, recordings or transcripts are published here. The dual-card tower photograph is historical build imagery, not the current AI guest specification.

A conversation is a pipeline.
  1. TELEPHONYPBX → incoming audio
  2. UNDERSTANDSpeech recognition → local language model
  3. RESPONDSpeech synthesis → caller audio
  4. TRANSFERSeparate, constrained transfer policy

Conceptual architecture. Operational addresses and configuration deliberately omitted.