Guest Review Intelligence for Doha's Luxury Hotel Market
An AI engine that reads 67,962 hotel guest reviews against an 84-aspect ontology, powering competitive analytics and portals for luxury hotels in Doha.
- 67,962
- reviews analyzed
- 285,344
- labeled observations
- 16
- hotels live
Luxury hotels sit on years of guest feedback they cannot read at scale. For Doha's luxury hotel market, we built an engine that reads all of it: 67,962 reviews spanning back to 2004, from four major booking and review platforms, turned into 285,344 structured observations.
The problem
A general manager knows the last bad review. Nobody knows whether housekeeping complaints are trending up quarter over quarter, whether breakfast sentiment beats the competitive set, or which specific aspects of the stay drive scores down. Star ratings average away exactly the detail that matters.
What we built
- Sentence-level AI labeling. An LLM reads every sentence of every review and labels it against a hospitality ontology we developed: 16 topics and 84 specific aspects, each with sentiment, intensity, severity, and confidence. The ontology went through 26 versions and over a thousand rule-set iterations to hold up against real text.
- Competitive-set analytics. Each property sees its own performance and an anonymized ranking across the 16 live hotels, aspect by aspect, with trend detection over time.
- A revenue-at-risk model that translates negative aspect trends into a directional financial estimate, anchored to published academic research on review-score elasticity, and always labeled as an estimate rather than a measured outcome.
- A tenanted client portal. Each hotel gets its own magic-link access to a fast web portal fed by a weekly automated pipeline; analysts work in a separate internal workbench.
How it works
The labeling pipeline is parallelized and checkpointed in atomic chunks, so a failed run resumes cleanly rather than re-billing the whole corpus. We benchmarked frontier and open models before committing; the configuration we shipped kept roughly 95 percent of top-model quality at about a quarter of the cost, and a 529-review backlog labels in about 25 minutes for roughly a dollar.
What it proves
Unstructured feedback becomes an auditable dataset: every chart drills down to the sentences behind it. This is our AI and NLP practice in one line: not a chatbot, but structure, at scale, you can verify.