What happens when the AI doesn't understand what the caller said?

Well-configured AI voice systems have graceful fallback handling: they ask for clarification once ('Sorry, I didn't catch that — could you repeat the project type?'), and if the answer is still unclear after a second attempt, they transition to an escalation path: 'Let me connect you with someone from the team' or 'Let me take your number and have someone call you back.' An AI that handles ambiguity gracefully is experienced as competent, not frustrating.

Can the AI voice agent be trained to recognise our specific services and terminology?

Yes — this is part of the knowledge base configuration during setup. If you do MEP engineering, tenant improvements, or post-tension concrete design, those terms and the relevant context go into the knowledge base. The AI's speech recognition is also primed with industry vocabulary so it accurately transcribes technical terms rather than substituting common-sounding alternatives.

✦ AI Voice

How Does an AI Voice Agent Answer Calls?

When an inbound call hits an AI voice agent, the call is answered in under one second, the caller's speech is converted to text, a language model generates a response based on the business's configured knowledge base, and a text-to-speech engine converts the response to voice. This cycle happens in 1–3 seconds per exchange, fast enough to feel like a normal conversation to the caller.

The technical stack of an AI voice call:

1. Call routing: incoming calls to your business phone number are forwarded to the AI voice platform (either as the primary answer or as an overflow when lines are busy). The forwarding uses standard carrier call forwarding — no changes to your existing phone system are required in most configurations.

2. Speech-to-text (STT): the caller's speech is transcribed in real time by a speech recognition engine. Modern STT systems have 95%+ accuracy for clear speech and handle regional accents, industry terminology, and names reasonably well when the model is primed with relevant vocabulary.

3. Language model processing: the transcribed text is processed by a large language model configured with your business's specific knowledge base — your services, service area, pricing ranges, process, and any specific handling instructions for different call types. The model generates the appropriate response.

4. Text-to-speech (TTS): the response is converted to natural-sounding speech by a TTS engine. Current TTS quality is high enough that the voice is perceptually close to human speech, with natural cadence and appropriate inflection.

5. Action execution: alongside the voice response, the AI can trigger actions — creating a calendar booking, sending a confirmation text to the caller, logging the call details to a CRM, or routing the call to a human.

The complete cycle from caller speech to AI response typically takes 1.5–3 seconds. Long pauses feel unnatural; the engineering challenge in AI voice is keeping the response time short enough to feel conversational without sacrificing response quality.

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