AI-Powered Review Management System for Hotels & Restaurants

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.

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

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.

NLP Development: Text Classification, NER, Embeddings, and Information Extraction

We often receive a task: process 50,000 support tickets — currently all manual. Dataset — 3,000 labeled examples, 12 categories, imbalance: one category occupies 40% of the sample, three at 1-2% each. Baseline accuracy — 78%. Sounds decent until you look at recall for rare classes: 0.31, 0.44, 0.28. These classes — complaints and churn threats — are most important to the business.

This is a typical NLP development project. The problem is not the algorithm but that accuracy is the wrong metric. Our experience across 30+ projects shows: we start by analyzing business metrics and only then choose the model.

Why accuracy is not the right metric for rare classes?

Accuracy ignores imbalance. If the "churn" class appears in 2% of cases, the model can predict "all good" and get 98% accuracy — but the business loses clients. Solution: F1 macro (averaged over all classes) or weighted F1. For NER — strict entity F1 (exact matches only). We guarantee: after choosing the correct metric, model quality becomes measurable and predictable.

Text Classification: From BERT to Distillation

BERT-like models are the standard for classification. ruBERT-base or ruBERT-large from DeepPavlov for Russian. multilingual-e5-large — for multiple languages in one pipeline. XLM-RoBERTa-large — a strong multilingual backbone.

Fine-tuning for classification: add a classification head on top of the [CLS] token, train for 3-5 epochs with lr=2e-5, weight decay=0.01. For imbalance — weighted CrossEntropyLoss or focal loss with gamma=2.0. Contact us — we will show a code snippet.

Imbalance case study. Dataset — 3,000 examples, imbalance 1:20. Solution: class_weight via sklearn + CrossEntropyLoss. Additionally — augmentation of rare classes via backtranslation (ru→en→ru through MarianMT). Recall for rare classes rose from 0.31 to 0.67 with a slight drop in accuracy (76%→74%). Full NLP development end-to-end took 3 weeks.

Distillation for production. BERT-large gives F1 0.89, but inference on CPU — 180ms. Distillation into DistilBERT or ruBERT-tiny2 reduces latency to 25ms with F1 0.84. Export to ONNX Runtime provides an additional 1.5-2x speedup. DistilBERT achieves 7x lower latency than BERT-large with only a 5% drop in macro F1 – a typical production trade-off.

How to choose between BERT and LLM for your task?

For most classification and extraction tasks, BERT-sized models offer the best trade-off between cost and performance. Shift to LLMs only when the task demands generation, complex reasoning, or zero-shot generalization.

NER: Named Entity Recognition

NER — extracting persons, organizations, locations, dates, amounts, document numbers. For general categories (PER, ORG, LOC), pre-trained models work well. For specialized ones (medical terms, legal concepts) — fine-tuning is needed.

Data annotation. The main cost of an NER project. For a quality model — 500-2,000 labeled sentences per entity type. Tools: Label Studio (open source) or Prodigy (by spaCy creators). IOB2 format — standard.

Architecture. Token classification on top of BERT: each token gets a label (B-PER, I-PER, O). spaCy 3.x with transformer pipeline — a convenient production choice.

Nested entities. Standard IOB models cannot handle nested entities (organization inside an address). For such tasks — span-based NER: SpanBERT or SpERT. More complex but correct.

Post-processing is mandatory. The model predicts tokens — normalized entities are needed. Date — dateparser. Amounts — regex + validation. Names — deduplication via rapidfuzz. Included in our standard delivery.

Sentiment Analysis and Opinion Mining

Binary classification positive/negative works out of the box with BERT. Complexity — aspect-based sentiment analysis (ABSA): "the restaurant has good food but terrible service." For ABSA: aspect extraction (NER) + sentiment per aspect. Joint models BERT-for-ABSA — quality on Russian data is lower due to dataset scarcity. RuSentiment, SentiRuEval — main resources.

For production with simple positive/negative/neutral: distil models are enough. Three classes, balanced dataset, 2,000+ examples — F1 macro 0.82-0.87 in 1-2 days.

Text Summarization

Extractive summarization (select sentences) — TextRank or BM25 without training. Fast, no hallucinations. Good for long documents.

Abstractive (generates new text) — seq2seq: mT5, mBART, FRED-T5, ruT5-large. For production via LLM API (GPT-4, Claude) — often the best cost/quality/speed trade-off.

Embeddings: Vector Representations of Text

Embeddings are the foundation of semantic search, deduplication, clustering, RAG. Quality critically affects downstream tasks.

Models. E5-large-v2, BGE-M3, multilingual-e5-large — strong multilingual embedders. sentence-transformers/paraphrase-multilingual-mpnet-base-v2 — fast option. For Russian: ru-en-RoSBERTa (Skoltech) performs well on semantic textual similarity.

Embedding quality evaluation uses the MTEB benchmark as standard. But top results on MTEB don't guarantee success on a domain dataset — we build domain-specific eval.

Fine-tuning embeddings. If standard models don't give the required Recall@k — contrastive learning on domain pairs with MultipleNegativesRankingLoss. How to perform this for domain data:

1. Collect 500–2,000 semantically similar pairs from your domain.
2. Apply MultipleNegativesRankingLoss with a batch size of 32–64.
3. Train for 1–3 epochs using AdamW (lr=2e-5).
4. Evaluate Recall@k on a held-out domain test set.

This approach yields a 5–15% improvement in Recall@k in practice.

Dimensionality and storage. E5-large: 1024 dim, float32 — 4KB per vector. For 10M documents — 40GB. Quantization int8 reduces to 10GB. FAISS IVF_PQ — more compact but with losses. Included in our deployment recommendations.

Information Extraction

Structured extraction is a frequent task. Examples: key contract terms, technical characteristics, dates and amounts from invoices.

1. Regex + rule-based. For INN, OGRN, amounts, dates — more reliable than neural networks. No data required.
2. NER + post-processing. For variable formats.
3. LLM with structured output. GPT‑4 / Claude with JSON schema — for complex documents. Cost: minimal per document. For 10k+ documents/day — we calculate the economics.

We guarantee a hybrid: regex/NER for typical fields + LLM for edge cases. Our guarantee is backed by years of production experience and more than 30 projects.

Work Stages

Prototype on existing data — 1-3 weeks. Production system with CI/CD — 1.5–2.5 months. Cost is calculated individually — get a consultation for a project estimate.

What's Included

• Model and pipeline architecture documentation
• Access to the model via REST API (FastAPI + ONNX)
• Client team training (2-hour webinar + Q&A)
• Accuracy guarantee on the agreed test set
• Months of post-delivery support (bug fixes, adaptation to new data)

Our Experience

Years of NLP projects from classification to RAG systems. The team includes ML engineers experienced with Hugging Face, spaCy, LangChain, MLOps. We use vLLM, Kubeflow, Weights & Biases — a production stack, not toys. Contact us to evaluate your NLP project within two days — request a free consultation on your text processing pipeline.