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Voice-Command-Based Restaurant Operations Management

Azati’s team developed a voice-command-based system that automates routine workflows in restaurants, ensuring efficient management through seamless task processing and speech recognition.

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92%

accuracy in recognizing core voice commands across multiple languages

50%

reduction in service delays

35%

faster task completion

All Technologies Used

Sentence-Transformer
Sentence-Transformer
API ChatGPT
API ChatGPT
Transformer
Transformer
Spacy
Spacy
NLTK
NLTK
Pandas
Pandas
Numpy
Numpy

Motivation

The customer needed to eliminate the constant operational chaos caused by misheard orders, inefficient task delegation, and the lack of a structured workflow. Staff often forgot tasks, misinterpreted verbal instructions during busy hours, and struggled to coordinate responsibilities in a fast-paced environment. The client sought a hands-free, accurate, and reliable solution that would streamline communication, automate routine actions, and reduce dependency on manual task tracking, ultimately improving service speed and consistency.

Main Challenges

Challenge 01
Multilingual Support

The system required proficiency in English, Spanish, and French. Mechanisms for recognizing and processing diverse language-based requests were designed to cater to multilingual customers.

#1
Challenge 02
Natural Language Processing

Customers and employees needed the freedom to issue voice commands naturally without adhering to strict formats. The system was trained to identify and interpret informal commands accurately.

#2
Challenge 03
Deployment on Resource-Limited Hardware

The customer requested a solution deployable on low-power devices such as laptops. Optimizing the machine learning model for resource efficiency was critical to ensure local deployment feasibility.

#3

Our Approach

Speech Data Capture & Audio Preprocessing
Collected voice samples from both customers and staff to reflect real restaurant acoustics: background noise, overlapping speech, clattering dishes, and varied microphone distances. Audio was cleaned using noise reduction filters, normalized, segmented into manageable chunks, and labeled according to command categories. This dataset became the foundation for accurate speech recognition and intent extraction.
Speech-to-Text Transformation
Integrated Whisper as the core ASR engine and fine-tuned it on domain-specific vocabulary (menu items, staff terminology, customer phrases). Whisper.cpp was applied to compress and quantize the model for deployment on low-power laptops without GPU acceleration. We conducted latency optimizations, reducing average command-to-text conversion time from ~820 ms to ~340 ms on commodity hardware.
Natural Language Understanding & Command Extraction
Implemented a multi-layer NLP pipeline combining NER models, command classifiers, and rule-based disambiguation. The NER model was trained on custom entities (e.g., TABLE_NUMBER, ACTION, ITEM, REQUEST_TYPE). The classifier distinguished between customer-generated and staff-generated commands. Additional logic resolved ambiguous phrasing such as 'Could you bring something else for table 2?' by extracting required attributes and mapping them to operational tasks.
Context, Emotion & Intonation Analysis
Analyzed text and acoustic features to differentiate polite requests from urgent or corrective commands. For example, tone-based indicators helped detect priority tasks like 'I need a waiter now' or 'The bill, please, quickly.' This ensured the system could escalate tasks and assign them to the nearest available staff member.
Model Training, Testing & Optimization
Constructed a command dataset covering over 40+ unique operational intents. Conducted iterative training, evaluation, and error analysis to reduce false positives (e.g., accidental triggers from casual conversation). Implemented quantization and pruning to reduce model size by 38% while maintaining accuracy. Benchmarked performance across three hardware classes to ensure smooth operation even on low-spec devices.
POC Development & Validation
Built a functional prototype demonstrating the complete pipeline: voice capture → ASR → NLP parsing → task generation → real-time updates. The POC included a monitoring dashboard visualizing queued tasks, timers, execution statuses, and error cases. We verified edge cases like overlapping speech, non-command phrases, multilingual transitions, and staff noise interferences.
Deployment Architecture & Integration
Developed local-first architecture ensuring the system works without stable internet: Whisper.cpp handled offline ASR; NLP ran on a lightweight local server; communication between devices used a low-latency websocket-based protocol. Created seamless integration with staff mobile devices, internal dashboards, and restaurant management systems for automated task distribution and completion tracking.

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Solution

01

Multilingual Voice Recognition

The system supports multiple languages, enabling restaurants to serve diverse clientele efficiently. By recognizing English, Spanish, and French commands, it ensures that all customer and staff requests are correctly processed without language barriers.
Key capabilities:
  • Language detection and automatic switching
  • Real-time multilingual transcription
  • Command recognition accuracy across all supported languages
02

Natural Voice Interaction

This module allows staff and customers to interact with the system using natural speech, without predefined phrases. Commands are interpreted contextually, including informal or incomplete sentences, providing a seamless and intuitive user experience.
Key capabilities:
  • Recognition of informal commands
  • Contextual understanding of speech
  • Real-time task creation from spoken commands
03

Optimized Low-Resource Deployment

Enables local deployment on laptops or low-power devices, making the system accessible without major infrastructure upgrades.
Key capabilities:
  • Efficient Whisper-based ML model
  • Lightweight deployment using whisper.cpp
  • Reduced memory and CPU usage
04

Task Automation and Monitoring

Automatically converts recognized voice commands into tasks assigned to staff members. Tasks are tracked in real-time with timers, reminders, and dashboards, allowing management to monitor operations, identify bottlenecks, and ensure timely completion.
Key capabilities:
  • Automatic task assignment to staff
  • Timers and reminders for task completion
  • Dashboard monitoring of all tasks and statuses

Business Value

Streamlined Restaurant Operations: Reduced manual task management and improved workflow efficiency.

Enhanced Customer-Staff Interaction: Enabled natural voice commands for a smoother dining experience.

Accessible Deployment: Optimized for low-resource devices, allowing wider adoption without infrastructure upgrades.

Validated POC: Demonstrated system readiness and performance for full-scale implementation.

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