AI itineraries: an honest read on what works and what does not

Every travel software company has launched an AI itinerary feature in the last eighteen months. Most of them are demos in disguise — they look impressive in a tweet and fall apart on a real booking. We took a year to ship Voyazio AI, and a few things became clear about where the technology actually earns its keep.

What LLMs are genuinely good at, today

• •Structuring messy enquiries. A WhatsApp message that says 'Goa for 5 nights, 2 adults 1 kid, sometime in November, beach side, budget 80k' becomes structured fields in two seconds.
• •Drafting first-pass itineraries with a sensible day-by-day rhythm — local food, light mornings, sightseeing windows that respect heat or rain.
• •Generating customer-facing prose. Itinerary descriptions, follow-up emails, post-trip thank-you notes. Edited by humans, but the blank page is gone.
• •Translating across English, Hindi, and regional languages with cultural sensitivity that matches operator-team writing.

Where they still hallucinate badly

1. 01Real-time pricing. They will confidently invent flight fares and hotel rates that do not exist. We never let an LLM quote prices — those come from booked supplier APIs only.
2. 02Visa rules. They will paraphrase 2019 immigration policy as if it is current. Visa logic is hard-coded against an updated rule engine, not generated.
3. 03Niche operator data. Small homestays, micro-tour operators, specific guide names — these are below the model's training horizon. We retrieve them from our database and inject them into prompts.
4. 04Conflict resolution between segments. The model will happily route you from Manali to Leh by road in November when the highway is closed. Constraint-checking is a separate pass.

How we designed Voyazio AI

Three layers. The model handles natural language understanding and generation. A retrieval layer pulls verified data — pricing, availability, visa rules, operator inventory — and injects it into the prompt. A constraint-checker validates the output before it reaches a human. The human still reviews every itinerary before it goes to a customer. We are not trying to remove people from the loop. We are trying to give each person back two hours a day.

What we are not doing

We are not building a customer-facing chatbot that books trips end-to-end. The mistakes get expensive — a wrong visa, a wrong terminal, a wrong hotel — and the recovery cost wipes out the efficiency gain. The bar for fully autonomous travel agents is much higher than the demos suggest. Until then, the right pattern is human + AI, not AI alone.