CorkRules
A restaurant and wine discovery platform focused on corkage policies, AI-assisted wine intelligence, menu scraping, restaurant search, location-based discovery, and customer dining experiences.
Project Overview
CorkRules is a restaurant and wine discovery platform designed to help users explore restaurants based on corkage policies, wine programs, dining preferences, and restaurant experiences. The platform centralized restaurant discovery, wine intelligence, customer search workflows, operational content management, and policy-driven dining recommendations into a unified ecosystem. The system enabled users to discover restaurants using advanced location-aware filtering, wine-focused search experiences, and corkage-policy intelligence workflows. Restaurants and operational teams could manage wine-related information, dining policies, restaurant content, and customer engagement workflows through operational dashboards. A major engineering focus was building intelligent wine-information extraction workflows powered by AI Vision systems and OpenAI APIs. Users could upload wine bottle images, labels, or menu-related content, and the platform automatically extracted wine metadata, labels, producer information, and structured wine details using OCR-assisted AI pipelines. The platform also included automated web-scraping workflows for restaurant menu aggregation, wine-list extraction, corkage-policy collection, and operational content synchronization. AI-assisted parsing systems processed scraped restaurant data, menus, and wine-related information to maintain structured restaurant intelligence datasets and improve customer discovery experiences. The platform also integrated ElasticSearch-powered restaurant discovery infrastructure to support high-performance filtering, location-aware recommendations, and scalable restaurant search experiences across growing operational datasets. Sidekiq-powered asynchronous background processing systems handled image-processing workflows, AI extraction pipelines, operational synchronization, search indexing operations, and scraping workflows without impacting user-facing platform performance. The system was built with scalable Ruby on Rails backend services, React-powered interfaces, PostgreSQL relational infrastructure, and Dockerized deployment environments to support operational scalability and intelligent customer discovery workflows.
Responsibilities & Contributions
- Architected scalable backend APIs and restaurant operational systems.
- Designed intelligent restaurant discovery and filtering workflows.
- Integrated ElasticSearch-powered restaurant and wine discovery infrastructure.
- Built automated menu-scraping and wine-list extraction systems.
- Developed web-scraping workflows for restaurant data aggregation and synchronization.
- Built AI-assisted wine detail extraction systems using OpenAI APIs.
- Developed OCR-assisted image-processing workflows for wine-label analysis.
- Implemented Sidekiq-based asynchronous scraping and AI-processing pipelines.
- Optimized PostgreSQL relational query performance for large restaurant datasets.
- Built scalable operational content management systems.
- Developed location-aware restaurant discovery workflows.
- Managed deployment infrastructure and Dockerized operational environments.
Engineering Challenge
Large restaurant datasets, complex location-aware filtering, wine-label image processing, and continuously changing menu information introduced performance bottlenecks and inconsistent metadata extraction workflows.
Technical Solution
Implemented ElasticSearch-powered discovery systems, Sidekiq background processing pipelines, OCR-assisted AI extraction workflows, automated scraping infrastructure, and optimized relational indexing strategies to improve search performance and structured restaurant intelligence accuracy.
System Architecture
Technical Decisions
Ruby on Rails
Powered scalable restaurant APIs, wine workflows, and operational business logic.
ElasticSearch
Enabled intelligent restaurant discovery, location-aware filtering, and high-performance search workflows.
Sidekiq
Managed asynchronous background jobs, AI workflows, and search indexing operations.
OpenAI APIs
Powered AI-assisted wine metadata extraction and contextual information processing.
AI Vision
Processed wine-label images and extracted structured wine-related information.
Web Scraping
Automated restaurant menu aggregation, wine-list extraction, and operational content synchronization workflows.
OCR
Extracted structured metadata and wine-related information from uploaded images and scraped restaurant assets.
PostgreSQL
Handled relational restaurant, wine, and operational platform datasets.
React
Delivered customer-facing discovery interfaces and operational dashboards.
Docker
Provided deployment consistency and scalable infrastructure portability.
Features & Capabilities
- Restaurant discovery workflows
- Wine & corkage intelligence
- AI-assisted wine detail extraction
- Restaurant menu scraping
- Automated wine-list extraction
- OCR-powered metadata extraction
- AI Vision-based wine-label analysis
- Advanced restaurant filtering
- ElasticSearch-powered discovery
- Location-based restaurant search
- Operational content management
- Restaurant policy management
- Customer dining discovery workflows
- Wine information management
- Realtime search indexing workflows
- Operational dashboards
- Scalable restaurant search infrastructure
Outcomes & Impact
- Built scalable restaurant and wine discovery infrastructure.
- Implemented automated restaurant menu and wine-list scraping systems.
- Improved restaurant search performance using ElasticSearch optimization.
- Implemented AI-powered wine-label extraction workflows.
- Centralized operational restaurant and corkage management systems.
- Designed asynchronous AI-processing pipelines for image analysis workflows.
- Enhanced customer dining discovery experiences using intelligent search infrastructure.
Engineering Focus Areas
Technologies CorkRules Uses
CorkRules is built using modern technologies carefully selected to optimize performance, stability, and scale.
Why we chose OpenAI APIs
Enabled AI-powered wine information extraction and contextual intelligence workflows.
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