9 days. 8 specialists. Zero HR headaches.

Guest reviews are one of the most honest data sources a hospitality business has. Guests write what they actually experienced, not what a survey prompted them to say. Across enough reviews, patterns emerge - recurring complaints about the same friction points, service highlights that drive return bookings, early signals of declining satisfaction before it shows up in occupancy rates.

This Swiss hotel group had all of that, sitting across TripAdvisor, Booking.com, Google and every other platform their guests used. Thousands of reviews, in multiple languages, across a portfolio of luxury and boutique properties. The team was reading them manually, synthesising them informally, and acting on them slowly if at all.

The gap was not data. The gap was the infrastructure to turn unstructured text into something a hotel operations team could query, act on, and track over time - without needing a data scientist in the room every time a general manager wanted to understand why guest satisfaction had shifted in a particular property.

Building an AI analytics platform on a brief like this requires a very specific combination. Engineers who have worked with LLMs and RAG architecture in production, not just in notebooks. A frontend team that can translate complex analytical output into interfaces a non-technical hotel manager can navigate without training. And enough domain awareness to understand what questions a hospitality operations team actually needs answered before designing the system around those questions.

That profile is narrow. A generalist engineering team would have built something technically functional that missed the operational context entirely. A data science team without product engineering depth would have produced insights with no accessible interface. The right team required both, assembled from the start rather than bolted together mid-engagement.

Talex put a shortlist of pre-vetted specialists in front of the client within days - each assessed for AI and NLP domain depth, relevant project experience, and the communication standard a Swiss enterprise client expects from an external team. The client selected every person through direct interviews. Talex managed the engagement throughout, so the hotel group's leadership focused on defining what intelligence they needed, not on coordinating the team producing it.

The platform was built with Flask on the backend and Next.js on the frontend. AWS handled hosting and ensured the solution could scale as the hotel portfolio grew. Web scraping tools integrated with Flask automated review extraction across all platforms. An NLP pipeline handled data cleaning: stop-word removal, sentiment normalisation, spell-checking - feeding a Vector Database structured for efficient retrieval. OpenAI LLMs and a re-ranking algorithm analysed the cleaned data, identifying recurring themes, service highlights and emerging trends. RAG enhanced the contextual accuracy of generated insights. The chatbot interface used OpenAI embeddings for semantic search, allowing operations staff to query the data in plain language and receive summaries or detailed responses in real time. Chart.js handled data visualisation across the dashboards.