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

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

Listen to this episode on: Spotify | Apple Podcasts

What does it take to build an AI that can take a food order over WhatsApp — correctly, every time, fast enough that customers can't tell it's not a person? That's the core challenge Santi Marchiori and Juan Haedo set out to solve at AITropos, a company building AI employees for the hospitality industry.

In this episode of Just Now Possible, Teresa Torres talks with Santi Marchiori (CEO) and Juan Haedo (CTO) of AITropos about how they built an AI order-taking agent that handles the full flow — menu recommendations, modifiers, delivery zones, payment links, and status updates — entirely inside WhatsApp. They went through three product iterations to get there: first a hardware device for waiters, then a waiter-facing app, and finally a customer-facing conversational agent powered by a tools-based architecture designed for speed and reliability.

You'll hear how they solved the core technical challenge of translating non-deterministic human conversation into structured POS-compatible order data, why they chose tools over MCP for agent architecture, how they pre-inject product context to cut latency before the agent ever makes a tool call, and why they test with thousands of agent-simulated customer conversations overnight before deploying to any real venue.

Show Notes

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

Chapters

00:00 Meet the Founders
00:59 What AITropos 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

Full Transcript

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