AI systems proven in a running kitchen.
EyeAI builds AI for retail stores and kitchens — computer vision, forecasting, and language systems that run with human-in-the-loop oversight and fail-safe modes. We build to address labor shortages and raise operational efficiency, and we test every system in live service before we ship it.
What we build
A suite of AI systems that integrate with existing retail infrastructure, built around customer experience, operational efficiency, and resource optimization.
Enterprise POS System
Point of sale with predictive inventory, automated night audits, and multi-tier cloud sync across stores. Works offline and runs on Linux, Windows, and macOS.
Central Fusion Server
Processes real-time video, sensor, and POS data into one picture of the store — security, inventory, and customer flow — with human-in-the-loop oversight and a fail-safe mode.
ReTech Store
Checkout-free shopping built on dual RTX 5080s and 24-camera fusion, with a PostgreSQL + pgvector database and a local-first architecture.
ReTech Kitchen
Kitchen OS and POS in one: front-of-house orders sync in real time to a smart kitchen display system, and inventory is deducted automatically as orders are cooked.
RAG Chatbot for Retail
A retrieval-augmented chatbot that answers customer questions from your product and inventory data, in multiple languages, with human monitoring and fail-safe responses.
Agentic AI
Agents that automate repetitive store tasks — scheduling, alerts, pricing, predictive maintenance — under mandatory human oversight and comprehensive fail-safe controls.
How the system fits together
One fusion server ingests store data streams and turns them into scheduling, security, and cost decisions — designed for integration with existing retail environments.
├── RETAIL_STORE_ENV
│ ├── CCTV/IP_Cameras -- (Video Streams)
│ ├── IoT_Sensors -- (Temperature, Humidity, Motion)
│ ├── POS_Systems -- (Transaction Data)
│ └── Wi-Fi/Network_Data -- (Foot Traffic, Device Count)
│
└── EYEAI_CENTRAL_FUSION_SERVER
├── Data_Ingestion_Layer -- (Kafka/RabbitMQ)
│ └── Real-time_Stream_Processing
├── Computer_Vision_Engine -- (Object Detection, Pose Estimation, Crowd Analysis)
│ └── [ReTech_Platform_Module]
├── Natural_Language_Processor -- (Customer Intent, Feedback Analysis)
│ └── [RAG_Chatbot_Module]
├── Predictive_Analytics_Engine -- (Demand Forecasting, Staff Optimization)
├── Autonomous_Agent_Module -- (Task Automation, Proactive Alerts)
├── Secure_Data_Lake -- (Encrypted Storage)
├── API_Gateway -- (Secure Integrations)
└── Intuitive_Dashboard -- (Actionable Insights for Retailers)
OUTPUTS:
├── Optimized Staff Scheduling
├── Enhanced Customer Flow & Checkout
├── Proactive Security Alerts
├── Personalized Customer Interactions
└── Reduced Operational Costs
A living laboratory
The EyeAI team runs its Kitchen OS as a living laboratory at Curry Creations, a real production kitchen. Research questions come from live service, systems ship to that kitchen, and we report what we measured.
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Language models in food operations
Never Invent an Ingredient: A Safety-Constrained LLM Entity-Resolution Cascade for Noisy Multilingual Procurement Receipts
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Human–AI collaboration
The Team of One: Engineered Organizational Memory in Solo-Operator-Plus-AI Software Maintenance
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Human–AI collaboration
First, Do No 86: Safety-Engineering an Always-Listening Voice Agent with Irreversible Authority in a One-Person Kitchen
Development foundation
EyeAI's human-in-the-loop and fail-safe principles are developed and tested on working prototype systems — starting with a restaurant-management prototype that became the foundation for everything we ship.
Development framework
AI architecture design
- Intelligent suggestion algorithms in development
- Advanced data processing capabilities
- Machine learning model training frameworks
Human-in-the-loop framework
- Mandatory human oversight for all AI decisions
- Approval and feedback systems
- Continuous learning from human input
Fail-safe architecture
- Fallback systems for AI unavailability
- Core operations run without AI dependency
- Error handling and recovery throughout
Development goals
"We're building AI systems that will help reduce operational waste through intelligent recommendations and predictive analytics."
"Our goal is to create AI that enhances human decision-making while ensuring complete operational control remains with business owners."
"We're committed to building responsible AI that augments human capabilities rather than replacing human judgment and oversight."
How a feature ships
Focus: intelligent systems that enhance human decision-making while keeping complete operational control with the operator.
Approach: comprehensive testing and validation before deployment.
Estimate your return
See the potential return from EyeAI in your store. The calculator covers theft prevention, operational efficiency, customer experience, and staff productivity.
- Theft prevention savings
- Operational efficiency gains
- Customer experience impact
- Staff productivity analysis
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Get early access to concept demos, development updates, and partnership opportunities as we build the next generation of retail AI.
Target launch
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