KitchenGuard AI combines environmental and workflow signals to identify rising operational risk in commercial kitchens and provide practical, real-time guidance to kitchen managers and shift leads.
The aim is simple: help teams see risk building while there is still time to act, instead of reviewing what went wrong after service.
Kitchen risk overview
Illustrative interface — not live data
34.8 °C
62 %
78 dB
14 open
Dinner peak
Medium
Elevated heat and backlog on the hot line — consider rotating one chef, prompting hydration and pausing non-essential prep.
Kitchen sensors
Temperature · Humidity · Noise
On-site gateway
Local collection
Cloud intelligence
Rules-based fusion
Risk assessment
Kitchen & station level
Manager / shift lead
Human decision
KitchenGuard AI was created around recurring operational challenges experienced in working commercial kitchens — the conditions that quietly raise the chance of an error long before anything visible goes wrong.
Heat, ventilation limits and sustained physical load build through service.
Ticket volume and backlog compress the time available for careful checks.
Long shifts, delayed breaks and back-to-back covers change how a team performs.
Procedures stay the same whether the kitchen is calm or at its busiest.
Operational risk is usually reviewed after service, not while it is rising.
KitchenGuard AI is designed to complement existing HACCP and food-safety procedures rather than replace them. Experienced staff remain in control of every decision.
Founder & Managing Director
Rupesh has more than five years of commercial kitchen experience across Nepal and the UK, including his current role as a chef at Hampton by Hilton. That hands-on experience of live service shaped the product around practical, real-world kitchen operations rather than theory.
MSc in Business Management, De Montfort University
Chief Technology Officer
Subash leads technical development across software architecture, the manager dashboard, the mobile application, sensor-data integration and the risk-fusion engine.
A deliberately complementary founding team: hospitality and kitchen expertise paired with software and technical expertise.
KitchenGuard AI is deliberately privacy-preserving. Risk intelligence is assessed at the kitchen, station and operational-zone level — never as individual employee monitoring. This constraint is a core product design principle, and a key reason teams are willing to have the system in their kitchen.
KitchenGuard AI combines sensor data, workflow information and risk intelligence into a single operational view — delivered through a manager dashboard, a shift-lead mobile app and recurring operational reporting.
Conceptual kitchen zones — illustrative only
Hot Line
highHeat + backlog
Prep
lowStable conditions
Pass
mediumTicket pressure
Managers see an overall kitchen risk level with a traffic-light indicator, station-level detail across hot line, prep and pass, and the main factors contributing to current risk — designed to be understood in seconds during busy service.
The platform does not simply display a high-risk warning. It translates risk into practical recommendations that stay within the everyday authority of a shift lead.
Review longer-term operational patterns rather than isolated moments, so changes can be made to how the kitchen is run.
A lightweight companion app built for use mid-service — a few taps, no distraction from the pass.
When an allergen order occurs during elevated kitchen risk, the platform can prompt additional verification guidance. It supports your allergen process — it does not independently guarantee allergen safety.
A concise report that turns a month of operating conditions into decisions about rota, layout and process.
Designed for larger operators managing multiple kitchens, providing comparison of operational risk trends between sites. This is a planned capability, not part of the initial MVP.
Six modules translate raw kitchen signals into a risk view an experienced manager can act on.
Combines temperature, humidity, ventilation conditions, noise, ticket volume, order backlog, shift duration and break delays into a continuously updated kitchen risk score.
Evaluates heat exposure, communication demand, order complexity and workload to identify stations experiencing elevated combined pressure.
Combines fatigue-related indicators, workload, kitchen risk and allergen orders to determine when additional allergen verification may be appropriate.
Recognises that risk varies by stage of shift — pre-service preparation, breakfast, lunch rush, dinner peak, banquet production and end-of-shift clean-down.
Assesses fatigue-related operational risk without individually monitoring employees through biometrics, wearables, facial recognition or video surveillance.
Converts detected risk conditions into practical actions: staff rotation, hydration, workload redistribution, allergen segregation, reduction of non-essential tasks and service-flow adjustments.
The initial MVP uses commercially available sensor components and mainstream cloud infrastructure to reduce technical risk and enable faster pilot deployment. Risk scoring is transparent and rules-based.
Temperature sensors
Humidity sensors
Noise sensor
Manual ticket-backlog input
On-site gateway
Cloud risk-fusion engine
Dashboard + mobile alerts
Roadmap capabilities are planned, not currently available.
KitchenGuard AI operates through recurring software subscriptions, sensor hardware and implementation, calibration and training services — with multi-site licensing for larger operators.
Eight connected stages take a kitchen from first assessment through to continuous, human-led operational decisions.
Assess
KitchenGuard AI evaluates the kitchen environment, operational setup and pilot requirements.
Install
A starter sensor kit is installed in appropriate kitchen locations and connected through an on-site gateway.
Calibrate
The system establishes a kitchen-specific baseline from normal operating conditions and workflow patterns.
Monitor
Environmental and workflow signals are collected continuously: temperature, humidity, noise, ticket backlog, shift duration, break patterns and service stage.
Analyse
The risk-fusion engine combines available signals into a continuously updated kitchen risk assessment.
Alert
When risk reaches defined thresholds, the platform presents an appropriate alert or recommendation.
Act
The shift lead or manager decides whether to rotate staff, redistribute workload, provide hydration, add allergen verification, adjust service flow or reduce non-essential work.
Learn
Responses and operational information are logged to support reporting, calibration and future model refinement.
Recurring software subscriptions, with sensor hardware priced separately so kitchens can start with a single site.
For single-site independent restaurant kitchens.
For single-site hotel kitchens.
For operators managing 5 or more kitchens under a commercial agreement.
Hardware — separate from software subscription
The lease is structured over three years.
Additional services
Pricing shown reflects the current business plan and may be refined as pilot and early customer data becomes available.
Independent restaurants and small hotel kitchens are the primary early commercial focus.
Independent Restaurants
Especially kitchens with complex dishes and significant allergen exposure.
Small Hotel Kitchens
Including branded budget and mid-market hotel kitchens.
Future Expansion
Contract catering operators, small multi-site restaurant groups, hotel groups, and care home or institutional kitchens as a longer-term adjacent segment.
Business plan projections
Forward-looking figures from the KitchenGuard AI business plan — not current company achievements or guaranteed results.
Year 1
~14 average active kitchens
£100,500
Year 2
~180 average active kitchens
£433,000
Year 3
~410 average active kitchens
£882,300
Straight answers on scope, privacy, current capability and cost.