Technical Overview: Master Chef AI Architecture
Application Specifications and Core Technology
Master Chef AI operates on a dual-input architecture combining speech-to-text processing with computer vision models. The application utilizes Web Speech API for real-time voice transcription, converting spoken ingredient lists directly into structured data arrays. The visual recognition module employs convolutional neural networks trained on over 500,000 labeled food images, achieving 94.3% accuracy in ingredient identification under standard kitchen lighting conditions.
The recipe generation system operates through a three-stage pipeline: ingredient parsing, nutritional analysis, and recipe synthesis. When users submit their available ingredients via voice or camera, the system cross-references a database of 12,000 verified recipes while applying constraints for dietary restrictions, cooking time preferences, and cuisine type selections. The application generates three distinct recipe options simultaneously, each with calculated serving sizes, preparation times, and difficulty ratings.
Version 2.4.1 includes enhanced allergen detection and improved handling of ingredient substitutions. The application requires iOS 14.0 or Android 9.0 minimum, with 65MB storage space and camera permissions for visual scanning functionality. Haptic R&D Consulting developed the proprietary ingredient-matching algorithm that reduces food waste by suggesting recipes based on expiration proximity and pantry optimization metrics.