Sole developer (architecture · implementation · deployment · ops)
AI Guest Communication for Hotels — Voice + Chat + Messenger
24/7 AI concierge for four UK venues—web chat, Messenger, and phone—answering guest questions, qualifying event leads, and handing warm handoffs to staff with a full audit trail.
Project gallery
Use the side arrows to browse; click an image for full size.
Rigorous eval suite before go-live; venues run it around the clock with structured lead capture instead of missed DMs and voicemails.
AI Guest Communication for Hotels — Voice + Chat + Messenger
Sole build — n8n-orchestrated stack for four UK hospitality venues: embedded web chat, Facebook Messenger, and AI phone reception (Vapi + Twilio). Same retrieval-backed answers and lead capture everywhere, with venue-specific knowledge and guardrails so guests get accurate answers—not a generic hotel bot.
The problem
Guests message and call outside staff hours; high-value event enquiries sit next to “where do I park?” and get lost. Teams need 24/7 coverage, consistent answers per venue, and qualified leads (with context) without doubling headcount.
What I shipped
- Three channels — Website widget (HTTP), Meta Messenger (signed webhooks + send API), voice (inbound Twilio → Vapi conversational layer).
- RAG over venue data — Embeddings + PostgreSQL / PGVector per venue (menus, packages, policies, FAQs) so replies stay grounded in that site’s facts.
- Lead capture — When intent is events / private hire / groups: structured fields, urgency hints, source channel, and logging to Google Sheets for the sales team (MVP CRM).
- Session memory — Postgres-backed conversation state so follow-ups (“any vegetarian options?”) stay in context.
- Business rules — e.g. table bookings redirect to the venue’s real booking flow; capture triggers tuned for high-intent threads, not every chat.
- Hosting & ops — Self-hosted n8n on DigitalOcean with HTTPS for webhooks; I owned deploy and ongoing tweaks.
Architecture (at a glance)
Inbound traffic from web, Messenger, and Twilio fans into n8n workflows that call OpenAI (chat + embeddings), PGVector retrieval, and persistence—then optional Sheets rows for leads. Voice adds Vapi on the telephony path with human transfer when needed.
Stack
n8n (self-hosted), OpenAI GPT-4.1-mini + embeddings, PostgreSQL + PGVector, Vapi + Twilio, Meta Graph API (Messenger), Google Sheets, DigitalOcean.
Quality & result
Before go-live I ran an automated eval suite (factual checks per venue, regression cases from real feedback, and multi-turn lead flows)—36/36 on the final Bellini run, with CSV exports for audit. In production, venues get round-the-clock guest handling and structured leads instead of missed off-hours DMs and voicemails.