This example project explores a practical AI receptionist product for appointment-heavy local businesses. The important idea is not “chatbot on a website.” The product pattern is AI-assisted front desk operations with real tenant-owned records behind it.
The Problem
Small service businesses answer the same questions every day: hours, services, prices, appointment requests, wait times, and follow-up details. Staff also need to know which conversations became leads, which customers need human help, and which requests are safe to act on.
The Approach
The architecture keeps Rails as the source of truth. Public channels such as a website widget, QR page, kiosk, voice session, or phone call create tenant-scoped conversations. AI can answer from approved knowledge and request tools, but Rails validates every action before creating leads, handoffs, check-ins, or appointment requests.
What It Shows
- Multi-tenant SaaS foundations with business-owner and SaaS-admin boundaries.
- AI tool calls treated as validated backend actions.
- RAG and memory used for answers, not as permission to mutate records.
- Usage events and billing checks built into the workflow.
Outcome
Example project: demonstrates how to turn AI chat into operational software with tenant boundaries, validated tool calls, audit logs, and staff handoff paths.