DATA TO A BETTER JOURNEY
In developmentAI travel and local guides
Bus Timetable Guides
Turn travel information into useful guidance.
A content project connecting bus data collection, search-interest analysis, summaries, and scheduled publication.
HOW IT COMES TOGETHER
The core workflow- 01
Sources and interest
- 02
Topic selection
- 03
Summaries and guides
- 04
Scheduled publishing
01 / THE QUESTION
The problem we set out to solve.
Interest in routes changes with time and location. Collecting information and keeping travel articles updated creates recurring editorial work.
02 / OUR APPROACH
Our approach.
Bus information and search trends help shape content topics and publishing. LLM summaries and template fallbacks allow the workflow to run in different configurations.
- 01
Bus information collection
A crawler gathers route-related material for the content workflow.
- 02
Topics shaped by interest
Search and trend data help identify the information travelers are looking for.
- 03
Summaries and draft content
OpenAI summaries are optional, with templates used when no API configuration is present.
- 04
Scheduled publication
Metadata checks and scheduled or repeated publishing organize ongoing content operations.
03 / INTELLIGENCE AT WORK
Where AI does the work.
LLMs assist with summarizing collected material and writing guides. Collection, metadata checks, and scheduled publication are separate automation tasks.
04 / WHERE WE ARE
Where the project stands.
The website and its collection and generation tools are being developed together. The focus is on turning sources and search interest into guides, rather than predicting real-time arrivals.