I want to bring together airline reviews from five well-known travel sites into one straightforward web application. The goal is a clean, no-frills interface where users can instantly compare carriers, filter opinions, and spot patterns without bouncing between multiple sources. Here is how I picture the workflow. Behind the scenes, five lightweight scraper or API modules pull only the review data—no schedules, no fare information. Because I do not need real-time feeds, these jobs can run on a timed schedule that you propose (daily or a few times per day is enough) and push everything into a single database. From there, the front-end should expose an advanced, filter-based search allowing people to drill down by airline, rating, review date, keywords, or any other practical field you think adds value. Designwise I am not chasing fancy visuals; clarity and speed trump polish. A responsive layout, sensible typography, and intuitive navigation will do the trick. What matters most is that someone visiting for the first time can land, type or choose a filter, and immediately see useful review summaries. Key deliverables: • Data-collection modules for each of the five travel sites, built to be easily extended when new sources appear. • A central database schema optimised for quick filtered queries on large text fields. • Search interface with filter controls (think airline, date range, star rating, sentiment, etc.) that returns results fast and can handle pagination. • Simple admin or dashboard screen where I can verify scraper status, trigger a manual update, or adjust the fetch schedule. I am open to your preferred tech stack—Python with BeautifulSoup or Scrapy for retrieval, Node or Django on the back end, ElasticSearch or PostgreSQL for search indexing, React or plain Vue for the front end, whatever you feel offers the best balance of maintainability and performance. Please outline which tools you would use, how you will structure the update schedule, and an estimated timeline to an MVP.