How I Built an AI Hotel Review Intelligence Platform in a Weekend (Prompts Included)
The author built an AI-powered platform called WrongStay to analyze hotel reviews more deeply than standard star ratings allow, focusing on hidden patterns in guest feedback. Using tools like the Claude API and Outscraper, the system extracts insights such as traveler mismatches, recurring complaints, and staff performance across languages and time. The platform currently covers hotels in Athens and Zurich, with a low monthly operating cost and a focus on verified Booking.com reviews.
- ▪WrongStay uses AI to analyze hotel reviews and uncover insights that star ratings typically obscure.
- ▪The platform relies on verified Booking.com reviews, which are pre-labeled by traveler type and split into pros and cons.
- ▪Data collection was done via the Outscraper API, enabling batch processing of up to 1,000 hotel URLs at once.
- ▪The core analysis uses a carefully designed prompt for the Claude API to detect patterns like buried complaints and temporal changes in guest sentiment.
- ▪The system runs on a stack including React, Express, PostgreSQL, and Chart.js, with infrastructure costs under $50 per month.
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/harrisgnr/how-i-built-an-ai-hotel-review-intelligence-platform-in-a-weekend-prompts-included-4fg4 |
| Publication time | Sat, 16 May 2026 16:25:56 +0000 |
| Retrieval time | 2026-05-16T16:40:19.008Z |
| Last seen | 2026-05-16T16:40:19.008Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 0dHJAmTO3f7D |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3916465) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } harrisgnr Posted on May 16 How I Built an AI Hotel Review Intelligence Platform in a Weekend (Prompts Included) #ai #buildinpublic #webdev #productivity Hotel Grande Bretagne in Athens has a 9.3/10 on Booking.com. Here's what that score hides: Small rooms appear in 22% of reviews across all traveler types. Guests still give 10/10. The pattern is consistent: acknowledge the room, pivot immediately to the Acropolis view to justify the score.
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