Step 1: Call routing
A customer calls your business number. The call routes through your phone provider (Twilio, RingCentral, your existing PBX) to the AI receptionist endpoint instead of a human or voicemail.
Step 2: Speech recognition (caller speaks)
As the caller speaks, audio streams in real time to a speech-to-text engine. Modern engines (Whisper, Deepgram, Speechmatics) achieve 95%+ word accuracy on clean audio, with sub-200ms latency for streaming partial transcripts.
Step 3: Reasoning (LLM decides what to do)
The transcript hits a large language model (typically GPT-4-class or better) configured with your business-specific system prompt, your scheduling rules, your knowledge base, and the conversation history so far. The model decides what to say next, what data to capture, and whether any tool calls are needed (e.g., "check calendar," "look up customer," "book appointment").
Step 4: Tool execution (real-time integration)
When the LLM decides to take an action — book an appointment, look up a record, take payment — it calls the corresponding integration. Native integrations talk directly to your CRM/EHR/scheduler API. The result comes back to the LLM in milliseconds.
Step 5: Text-to-speech (agent responds)
The LLM's response goes through a text-to-speech engine (ElevenLabs, OpenAI Voice, your custom voice). The output is natural-sounding speech with the right prosody, pauses, and brand voice — streamed back to the caller in under 200ms.
Step 6: The whole loop, every 200–800ms
Steps 2–5 cycle continuously. The caller experiences a real conversation with sub-second response times. Behind the scenes, every word is captured, structured, and synced to your CRM in real time.
Step 7: After the call (analytics + improvement)
Once the call ends, the full transcript is scored for sentiment, tagged by topic, evaluated against your success criteria, and synced to your CRM. The Co-Pilot loop reviews every conversation and feeds learnings back into weekly model retraining and script refinement.
Frequently Asked Questions
- How fast does an AI receptionist actually respond?
- Top-tier deployments achieve 400–800ms end-to-end latency from caller speech to agent response. Anything under 1 second feels natural; anything over 1.5 seconds feels broken.
- Can it handle my industry's specific vocabulary?
- Yes. We train custom vocabulary on every deployment — medical specialties, legal practice areas, automotive parts numbers, HVAC trade terms, financial product names. The model learns your terms during the build phase.
- What if the caller says something unexpected?
- Modern LLMs handle unexpected input well — the agent acknowledges, asks a clarifying question, or escalates to a human if it's outside scope. Edge cases are handled by configurable escalation rules defined during discovery.