Original Summary

We recently moved our AI receptionist from a generic demo toward something a real service business can operate. The interesting work was not making the voice sound more human. It was defining where automation must stop. Here are five decisions that helped: Separate intake from commitment. In an auto-repair workflow, the system can schedule a vehicle drop-off, but it should not promise when the repair will be finished. Diagnosis, parts availability, authorization, and technician workload all affect completion. Model resources, not just calendar slots. A 30-minute oil-change team, a diagnostic technician, and a general-repair technician are different resources with different skills, working hours, breaks, blackout periods, and default durations. Preserve historical truth when templates change. Calls and transcripts may reference old FAQs. Replacing a business template should not delete or corrupt those historical records. We had to decouple old references and make template replacement atomic. Put hard limits on public demos. Our public number limits calls per caller and maximum call length, while customer production numbers remain unaffected. Demo abuse is a product requirement, not an edge case. Measure outcomes rather than conversation quality alone. The metrics that matter are qualified leads captured, appointments booked, transfers completed, escalation accuracy, and incorrect commitments avoided. We are building this as AdvanVoice AI. The current live demo uses auto repair as one example, but the core system is configurable for different service businesses. For other vertical SaaS founders: what business rule or exception turned out to be much harder than the AI itself?   submitted by   /u/EitherwayTed [link]   [comments]

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  • 信息来源：Reddit · SaaS
  • 发布时间：2026/9/13 22:37:22