Building AI Employees for Hospitality: How AITropos Takes Orders Where Customers Already Are

Guests

• Santi Marchiori, CEO, AITropos
• Juan Haedo, CTO, AITropos

You'll hear how they

• Spent two years exploring hundreds of startup ideas before finding the specific niche of AI-powered order taking in hospitality
• Went through three product iterations — hardware for waiters, a waiter app, and finally a customer-facing WhatsApp agent — before landing on the right form factor
• Identified order item identification accuracy as their single most important KPI
• Chose a tools-based agent architecture over MCP or pipelines to hit real-time response speed requirements
• Built a parallelized pipeline that searches for multiple products simultaneously and pre-fetches product context before the agent even calls a tool
• Use smaller, fast sub-agents to build an "immediate system prompt" that injects relevant data into each turn without extra tool calls
• Test with thousands of agent-simulated customer conversations run overnight before deploying to new venues
• Reduced new customer onboarding from three months to a few weeks — and continue to shrink it as they build domain templates

Resources & Links

• AITropos

Chapters:
00:00 Meet the Founders
00:59 What Tropos Builds
01:51 AI vs Human Touch
06:17 Restaurant Use Cases
08:16 Why Hospitality
10:47 Finding the Wedge
16:00 Early Prototypes
16:46 Hard Parts of Ordering
18:03 Speed and Channels
21:15 Iteration and Model Jumps
30:50 Customer Order Flow
35:48 Menu Discovery Question
36:07 Menus Inside WhatsApp
36:50 Finding the Chat Entry
37:37 Why Text Ordering Wins
38:30 Under the Hood Pipeline
40:54 Tools Over Workflows
45:05 Tooling and Prompt Composer
49:29 Preloading Context Fast
54:02 Founder Learning Mindset
57:21 Evaluating Order Accuracy
01:00:03 Testing and Human Takeover
01:03:56 Onboarding and Scaling Up
01:06:10 Whats Next and Wrap