The biggest myth about AI receptionists: that they're all the same

We build dolfyn, custom AI phone receptionists, and the assumption we run into most is that every AI receptionist is basically the same. In practice the gap is huge, and it comes down to how the call is actually handled.

Most tools are one generic bot reading a script you configure yourself. That falls apart on the calls that matter. Someone with a real emergency, like no heat in winter, needs a completely different conversation than someone asking about pricing, and a single bot trying to do everything tends to either interrogate people or miss the important part.

Our take is that it should work like a front desk, not a phone tree. We build a team of specialist agents, each trained for one kind of call (new leads, existing customers, after-hours emergencies), with an intake step that routes each caller to the right one. Each agent is trained on that specific business, and a real person reviews the calls every day and keeps tuning it.

Two things we've learned: accuracy comes from specialization, not from a longer prompt, and the craft is making a call flow naturally instead of stuffing it with questions until it feels like an interrogation.

Curious how others here think about single-agent versus multi-agent setups for voice, and where you've seen AI calls go wrong.