AI-Powered Wine & Restaurant Discovery Platform
CorkRules
Large restaurant datasets, complex location-aware filtering, wine-label image processing, and continuously changing menu information introduced performance bottlenecks and inconsistent metadata extraction workflows.
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 profile
Role
Full Stack Engineer
Timeline
2022 - 2024
Client/Company
CorkRules Inc
Stack
Ruby on RailsReactPostgreSQLDockerElasticSearchSidekiqOpenAI APIsAI VisionWeb ScrapingOCR
ElasticSearch
Search Infrastructure
Vision + OCR Extraction
AI Systems
Rails + React
Primary Stack
Overview
A deeper summary of the product, system, or engagement.
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.
Architecture and solution
Core summary
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. React Frontend, Ruby on Rails APIs, ElasticSearch Infrastructure, Web Scraping Pipelines, AI Vision Processing Pipelines, OpenAI Integration Layer, Sidekiq Worker Systems, PostgreSQL Database, Dockerized Infrastructure
Highlights
- 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.
- React Frontend, Ruby on Rails APIs, ElasticSearch Infrastructure, Web Scraping Pipelines, AI Vision Processing Pipelines, OpenAI Integration Layer, Sidekiq Worker Systems, PostgreSQL Database, Dockerized Infrastructure
Capabilities and focus areas
The detail pages now surface the delivery depth, not just the headline summary.
Features and capabilities
- 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 focus areas
- Backend architecture & APIs
- Infrastructure & deployment workflows
- Performance optimization
- Realtime WebSocket coordination
Outcomes and impact
A more complete record of what the work enabled or improved.
- 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.
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