Transforming Restaurant Operations with AI: TableWatch from DifiNative Powered by Intel Edge Processing
November 25, 2025
Sponsored Blog
The restaurant and hospitality industry has always been dynamic, but today’s environment is especially unforgiving. Rising commercial real estate costs, which often increase by 8–15% year over year in major cities, combined with escalating labor expenses and talent shortages, make profitability more challenging than ever. Despite extended operating hours, most restaurants still rely on a narrow peak window, typically accounting for less than 25 percent of the day, which contributes 60 to 70 percent of their revenue. During those hours, maintaining fast and consistent service becomes mission critical.
Supervising every table in real time is nearly impossible, particularly when a single missed opportunity to reset a table can directly translate into lost revenue. For chains with dozens or hundreds of outlets nationwide, the complexity compounds—varying layouts, staffing levels, and customer behaviour create operational blind spots that impact both guest satisfaction and profitability.
This is why digitization is now indispensable. Operators need accurate visibility and actionable insights at the speed of service. Digital twins of outlets, combined with short-term operational analytics and long-term business intelligence, are rapidly becoming essential tools for competitiveness.
Introducing TableWatch: Intelligent Table Management with Vision AI
To solve these operational challenges, DifiNative has created TableWatch, powered by SquirrelVision. It uses real-time vision AI to automate table visibility and streamline guest flow.
TableWatch helps restaurants:
- Reduce wait times by instantly identifying available tables
- Improve staff productivity with AI-guided prioritization
- Boost revenue by 5–10 percent through fast table turnover
- Maintain operational consistency across multiple outlets
Using live video feeds, TableWatch detects occupancy, understands service status, and tracks table readiness through visual cues. Simple dashboards guide staff to seat, serve, and reset tables more efficiently, acting as a silent supervisor that keeps operations running smoothly even when teams are stretched thin.
Optimized with Intel AI Edge Technology
Running fully at the edge, TableWatch delivers low-latency insights with strong privacy protection and no reliance on continuous cloud connectivity. TableWatch leverages Intel’s ecosystem for high-performance edge AI:
- Intel Core processors with Intel UHD Graphics enable fast, low-latency visual processing.
- OpenVINO toolkit accelerates AI inference with INT8 precision
- Dynamic CPU scaling ensures performance without wasting energy.
This architecture ensures responsiveness, data-sovereignty compliance, and scalability from a single outlet to nationwide deployment.
SquirrelVision: Turning Visual Data into Digital Twins Across Industries
While TableWatch focuses on hospitality environments, SquirrelVision is built to serve multiple verticals with the same core capability: transforming visual signals into structured business intelligence.
SquirrelVision can create dynamic digital twins for:
- Restaurants, cafes, hotels
- Retail store shelves, queues, and visual merchandising
- Manufacturing assembly lines and employee safety zones
- Warehouses, loading docks, logistics access points
- Public infrastructure, campuses, power, and utility plants
From occupancy and workflow status to safety insights and operational compliance—any visually derived data becomes part of a continuously updated, AI-powered digital representation of the real world.
Intel’s AI Edge Initiative
This blog is part of a series outlining Intel’s AI Edge initiative. Intel recently unveiled its Intel® AI Edge Systems, Edge AI Suites, and Open Edge Platform. These are designed to help partners integrate AI into existing infrastructure and help them jumpstart development and increase trust in their system performance and security.
Intel is collaborating with its software partners to create and optimize AI for edge applications, as illustrated in this series of blog posts. Intel is working with its hardware platforms to specify AI Edge systems that allow for best-fit AI performance for key AI edge workloads and are available in a variety of power levels, sizes, and performance options.
To find out more, visit Intel Solution Hub Applications for Edge AI.
Ken Briodagh is a writer and editor with two decades of experience under his belt. He is in love with technology and if he had his druthers, he would beta test everything from shoe phones to flying cars. In previous lives, he’s been a short order cook, telemarketer, medical supply technician, mover of the bodies at a funeral home, pirate, poet, partial alliterist, parent, partner and pretender to various thrones. Most of his exploits are either exaggerated or blatantly false.
