Mahin Mazhar

I m building Reception AI, and one problem I ve found much more interesting than simply making an AI answer the phone is deciding what it should do when it doesn t know something. Imagine a caller asks: Do you offer emergency appointments on Sundays? If that information isn t in the approved knowledge, I don t think the AI should guess. A confident wrong answer can be much worse than admitting uncertainty. So we re exploring a Knowledge Gap approach: Caller question AI detects uncertainty safe fallback knowledge gap gets flagged business provides the correct answer For higher-risk information like pricing, cancellation policies, availability, or promised callback times, the system could use stricter fallback rules. I m curious how other founders and AI builders would approach this: When an AI receptionist doesn t know something, should it immediately transfer to a human, collect the caller s details for follow-up, or use a predefined fallback and continue the conversation? I m especially interested in how you d balance customer experience vs. preventing confident misinformation.

I m building Reception AI, a web application that helps local businesses stop losing customers from missed calls. Reception AI can answer calls 24/7, have natural conversations, qualify leads, capture caller details, and book appointments. It can also send SMS confirmations and provide call transcripts. One feature we re working on is website-to-receptionist setup: enter a business website URL, use the available information to create the receptionist s initial knowledge, then review and approve it before going live. We re also exploring a Knowledge Gap Engine that can flag questions the AI couldn t confidently answer instead of making something up. The goal is simple: fewer missed opportunities more qualified leads more booked appointments. I m sharing this for feedback from other founders, business owners, and agencies. I d love to hear what you d want an AI receptionist to handle before you d trust it with your customers.