Aimpoint Digital builds AI agent travel itinerary system on Databricks using multi-RAG architecture
Planning a trip is time-consuming and overwhelming: travelers spend over 5 hours researching and visit up to ~270 web pages before finalizing activities. Standalone LLMs like ChatGPT suffer from a recency issue — they may recommend closed or outdated venues — and are prone to hallucination.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Traveler submits parameters
A traveler submits preferences including destination city, country, dates, travel purpose, companions, and budget.
Tools used
Databricks Data Intelligence PlatformDatabricks Mosaic AI Vector SearchDBRX InstructMeta-Llama-3.1-405b-InstructDSPyDelta tablesMLFlow
Outcome
The tool demonstrated transformative potential in the travel industry and received overwhelmingly positive feedback from stakeholders who appreciated the seamless planning experience and accuracy of recommendations. Itineraries are generated within seconds.
What failed first
Standalone GenAI tools like ChatGPT generate itineraries that can be misleading or incorrect because the underlying LLMs lack up-to-date information, suffer from a recency issue, and may hallucinate inaccurate details such as recommending a restaurant that has since closed.