Imagine: your hotel receives a negative review on Booking on Friday evening. Without AI, a manager notices it only on Monday — lost loyalty and a missed chance to fix the impression. We develop an AI system that monitors reviews 24/7, analyzes sentiment by aspect, and generates personalized responses. With over 5 years in HoReCa, we've deployed such solutions for 20+ properties, cutting the average response time from 48 hours to 2 hours. According to Booking.com data, a quick reply to a review increases the probability of re‑booking by 15%.
Problems the system solves
Information noise: managers spend up to 3 hours a day checking reviews across 5+ platforms. AI aggregates everything into a single dashboard. Delayed reaction: every hour without a reply to a negative review reduces the re‑booking probability by 5%. Inconsistent responses: different staff members write in different styles — the system unifies tone of voice without losing individuality.
How AI cuts response time
The system continuously checks for new reviews via webhook or API polling every 5–15 minutes. Upon a negative review (score 1–3), an alert is immediately sent to the manager, and the AI generates a draft reply in seconds. Using an LLM with a generation speed of up to 50 tokens per second, the time from receiving a review to a ready reply does not exceed 10 seconds. Compared to the manual process, this is 24 times faster — 2 minutes versus 48 hours.
Why aspect-based analysis matters more than overall sentiment
An overall score does not show what exactly displeased the guest. Aspect-based analysis breaks the review down into categories: cleanliness, service, location, cuisine, infrastructure. A fine-tuned LLaMA 3 model, trained on 50,000 Russian-language reviews, assigns each category a score from 1 to 10. This allows targeted improvement of weak spots — for example, if cleanliness score drops, you can double‑check housekeeping.
| Criterion | AI system | Manual process |
|---|---|---|
| Response time | < 2 hours | 48 hours |
| Response rate | 95%+ | 50–70% |
| Analysis | Aspect-based, 10 categories | Overall score |
How our system differs from standard solutions
Off-the-shelf monitoring services (Reputation.com and the like) offer generic reply templates and limited analytics. We build a custom ML platform for your brand. We use RAG (retrieval-augmented generation): the model loads past interactions and company policy so that the reply takes previous cases into account. For example, if a guest repeatedly complains about noise, the system offers an apology and reminds about measures already taken.
System components
| Component | Details |
|---|---|
| Platform integration | Booking, TripAdvisor, Google, Yandex, 2GIS — via official APIs or parsing |
| Aspect analysis model | Fine-tuning LLaMA 3 or GPT-4o on your historical reviews |
| Reply generator | Prompt engineering + RAG with your hotel/restaurant knowledge base |
| Dashboard & notifications | Web interface, Telegram bot, alerts for sharp rating drops |
| Documentation & training | Manuals for managers, API documentation, 3 hours of online training |
| Warranty & support | 1 year of technical support, SLA on incident resolution time |
Implementation process
Stages take 2 to 8 weeks, depending on the number of platforms and depth of customization.
Analytics (3–5 days): audit current processes, collect historical reviews, define success metrics. Design (5–7 days): choose the stack (PyTorch/HuggingFace for models, FastAPI for backend, ClickHouse for analytics), design the RAG pipeline architecture. Development (10–20 days): integrate APIs, fine-tune the model, develop UI, set up CI/CD via GitHub Actions. Testing (3–5 days): A/B test replies — AI vs human, measure response rate and time. Deployment (2–3 days): deploy on your infrastructure or in the cloud (AWS/GCP), set up monitoring.
What you get after implementation
- Reduced time-to-response to 2 hours (instead of 48)
- Increased response rate to 95%+
- Improved average rating by 0.3–0.5 points over 3 months
- Saved 40+ hours of manager work per month
Reputation management KPIs
The dashboard displays the following metrics:
- Average rating on each platform by month
- Response rate: % of reviews with replies
- Response time: average reply time
- Sentiment score per aspect (trends)
- Review velocity: number of new reviews per week
- Share of negative reviews (scores 1–3)
Example reply generation
def generate_review_response(review: Review) -> str: system_prompt = f"""You are the manager of {review.property_name}. Reply style: professional, warm, not template-like. Always: thank for the review, address specific details, on negative — acknowledge the problem and explain what has been/will be done.""" prompt = f"""Write a reply to the review: Score: {review.rating}/10 Text: {review.text} Date: {review.date}""" return llm.generate(prompt, system=system_prompt, max_tokens=200) The reply references specific details from the review — "We're glad you enjoyed the mountain view from room 304" is better than "Thank you for your review." Additionally, we implement guardrails that prevent promises that cannot be fulfilled.
Want a similar system for your hotel or restaurant? Contact us — we'll assess your project for free and provide demo access to a working system.