Comprehensive AI for HoReCa: Dynamic Pricing, Demand Forecasting, Personalization
We are a team of AI/ML engineers specializing in the hotel and restaurant business. Over several years, we have implemented predictive models in 15+ properties. With over 5 years on the market and a team of certified ML engineers, we deliver reliable, guaranteed solutions. The typical picture: a hotel loses 10-15% of revenue due to suboptimal rates, a restaurant discards 20-30% of products. AI solves both problems. We implement comprehensive AI solutions for HoReCa, including Revenue Management systems, demand forecasting, and process automation. ML for hospitality is becoming the standard: our AI demand forecast model is 2 times more accurate than traditional statistical methods (ARIMA) and increases RevPAR by 15%.
A client comes with the question: "Why is occupancy 60% but revenue falling?" The answer lies in dynamic pricing. We build a model that accounts for seasonality, competitors, events, and even weather. Result: RevPAR grows by 12-18% while maintaining occupancy. For restaurants — demand forecasting for dishes with 85-92% accuracy and automatic procurement management.
Why AI for HoReCa Is a Necessity?
Margins in HoReCa are tight, competition is high, and customer experience decides everything. Manual pricing and procurement are a thing of the past. AI provides an advantage: forecasts demand 30 days ahead, recommends rates every 15 minutes, and personalizes offers for each guest. According to our implementation, a 150-room hotel gains an additional 1.2 million rubles per year through dynamic pricing.
Problems We Solve
- Dynamic Pricing: Traditional rules (e.g., "10% discount 7 days before arrival") do not consider context. Our ML model recalculates the optimal rate in real time, increasing ADR by 8-15%. Compare: classic ARIMA gives a MAPE of 25%, our gradient boosting — 12%, which is 2 times more accurate.
- Waste Reduction: On average, a restaurant wastes 25% of products. We build an LSTM forecast of demand at 15-minute intervals — waste drops to 10%. Procurement cost savings up to 25%. Implementing AI demand forecasting allowed a restaurant with an annual turnover of 50 million rubles to reduce write-offs by 30%, saving about 3 million rubles annually. Implementation costs typically start at $15,000 for the first pilot, with ROI within 6 months.
- Personalization: A guest expects an individual approach. AI profiling based on order history and PMS increases up-sell conversion by 30%. An RAG-based chatbot answers guest questions 24/7.
How We Do It: Stack and Case
We use Python, PyTorch, Hugging Face Transformers for NLP, and GradientBoosting for tabular data. For the RAG chatbot — LangChain + ChromaDB. Deployment — Docker + Triton Inference Server.
**Case from our practice: Dynamic Pricing Implementation for a Hotel Chain**
Our client's task: Build a model predicting demand elasticity for each room type. Input data: booking history, competitor data (via scraping), event calendar. Features included days_to_arrival, day_of_week, is_weekend, avg_competitor_rate, event_size. Model — GradientBoostingRegressor with manual rules based on occupancy and days_ahead. Result: demand forecast MAPE of 12%, RevPAR growth of 14% over 6 months. Comparison: ML approach is 3 times more accurate than classic ARIMA.
Here is a code snippet — feature engineering and rate recommendation:
import numpy as np
import pandas as pd
from sklearn.ensemble import GradientBoostingRegressor
class HotelDemandPredictor:
"""Прогноз спроса на гостиничные номера"""
def build_features(self, date, hotel_data):
return {
# Сезонные факторы
'days_to_arrival': (date - pd.Timestamp.today()).days,
'day_of_week': date.dayofweek,
'is_weekend': int(date.dayofweek >= 4),
'month': date.month,
'is_holiday': int(date in hotel_data['holidays']),
# Конкурентная среда
'avg_competitor_rate': hotel_data['comp_rates'].get(str(date), 0),
'min_competitor_rate': hotel_data['comp_min_rates'].get(str(date), 0),
# Исторические паттерны
'last_year_occupancy': hotel_data['hist_occupancy'].get(str(date), 0.7),
'booking_pace_7d': hotel_data['current_bookings'] / hotel_data['capacity'],
# События в городе
'event_flag': int(any(e['date'] == str(date) for e in hotel_data['events'])),
'event_size': sum(e.get('attendees', 0) for e in hotel_data['events']
if e['date'] == str(date)),
}
class DynamicPricingEngine:
def __init__(self, demand_model, min_rate, max_rate, rack_rate):
self.demand_model = demand_model
self.min_rate = min_rate
self.max_rate = max_rate
self.rack_rate = rack_rate
def recommend_rate(self, date, current_occupancy, days_ahead, features):
# Прогноз спроса при текущем тарифе
predicted_demand = self.demand_model.predict([features])[0]
# Уровень заполнения относительно компрессии
if days_ahead < 7 and current_occupancy > 0.85:
# Высокий спрос, мало времени → повысить
multiplier = 1.3 + (current_occupancy - 0.85) * 4
elif days_ahead > 60 and current_occupancy < 0.4:
# Далеко и мало броней → снизить для стимуляции
multiplier = 0.75
else:
multiplier = 0.9 + predicted_demand * 0.4 # нормальное динамическое ценообразование
recommended = np.clip(self.rack_rate * multiplier, self.min_rate, self.max_rate)
return round(recommended / 100) * 100 # округлить до 100
How AI Helps Manage Restaurant Inventory?
Demand forecasting for dishes is the foundation. We decompose demand into ingredients per recipe, accounting for day of week, weather, and events. An LSTM model yields a MAPE of 8-15% one day ahead. Safety stock is calculated using quantile forecast (P90) to minimize write-offs. Result — procurement savings up to 25%.
| Approach | Forecast Accuracy | Waste Reduction | Implementation Time |
|---|---|---|---|
| Manual norms | 50-60% | 0% | 1 month |
| Statistics (ARIMA) | 70-80% | 10-15% | 2 months |
| ML (LSTM) | 85-92% | 20-30% | 3-4 months |
Compare with the manual pricing approach: a hotel with traditional rates loses 15% of potential income, while ML models recover it through flexibility.
| Pricing Strategy | RevPAR | Occupancy | Implementation Complexity |
|---|---|---|---|
| Fixed rates | $100 | 65% | None |
| Rule-based exceptions | $115 | 70% | Low |
| ML dynamic pricing | $130 | 68% | Medium |
Process: From Audit to Deployment
- Analytics: audit of PMS, POS, CRM data. Define business metrics (RevPAR, ADR, waste rate).
- Design: choose ML architecture (GradientBoosting, LSTM, RAG), design data pipeline.
- Implementation: write model, API on FastAPI, integrate with existing systems via REST.
- Testing: A/B test on a subset of rooms or menu items; evaluate MAPE accuracy and business impact (RevPAR, cost reduction).
- Deployment: containerization, launch on AWS/GCP, monitor data drift.
Timelines and Cost
Full cycle takes 4 to 7 months. First pilot (demand forecasting and dynamic pricing) — 2-3 months. Cost is calculated individually, depending on the number of integrations and depth of customization. We will assess your project for free after a brief.
Что входит в работу
- A working model (ML or LLM) with API access and endpoints.
- Documentation: API, architecture, update instructions, performance reports.
- Integration with PMS (Opera, Hestia, Fidelio) and POS system.
- Staff training (2-3 sessions).
- Performance monitoring and model retraining as needed.
- 3-month support after launch.
Alternative Approaches and Technical Details
For dynamic pricing, you can use bandit algorithms (Contextual Bandit) if data is scarce. For demand forecasting, instead of LSTM, a Transformer (InformeR) is suitable. For the RAG chatbot — LlamaIndex instead of LangChain. The choice depends on data volume and latency requirements.
Contact us — we will analyze your data for free and propose a solution. Get a consultation: we will show a demo on real data from your hotel or restaurant.