Cloud-native architecture
Context
The situation before the platform
Why restaurant operators needed one connected system instead of a POS, a spreadsheet and a stack of paper tickets.
| The business |
Restaurants, cafes and eateries of every size — from single outlets to multi-branch chains — alongside the diners who browse menus, reserve tables, order and engage with those venues. |
| The starting point |
A fragmented operational environment. No unified platform connected front-of-house, kitchen, inventory and delivery, so teams worked in silos with disconnected tools and manual coordination between them. |
| The trigger |
Orders and inventory tracked manually or through systems that never synced with stock levels, causing stock-outs of popular items, over-ordering of the rest, and revenue lost to miscommunication and duplicated tasks. |
| What they were looking for |
One centralised, multi-tenant platform where menus, staff workflows, orders, payments, loyalty and analytics all resolve against the same data — usable by a single outlet and by a franchise group from one dashboard. |
| Constraints |
Interfaces had to work in a fast-paced, high-pressure service environment on kitchen displays and handheld POS devices · updates had to synchronise instantly across every connected device and location · each outlet needed local flexibility without breaking group-wide standardisation. |
System
What it runs at today
The platform as delivered, live across web, mobile, POS and kitchen displays.
Staff productivity
26%
Uplift reported after scheduling, coordination and routine reporting were automated
Core modules
5
Live menu management, intelligent inventory, multi-location control, real-time analytics and loyalty
Engagement length
Long-term
A dedicated product team through build, launch and continuing scale
Team size
6+
Design, engineering and QA covering web, mobile, backend and infrastructure
The engineering problem
Five gaps that shaped the build from day one
Not vague pain points — the specific breaks in a restaurant's operating day, each paired with what we did about it.
0
1
No single platform across floor, kitchen, stock and delivery
Front-of-house activity, kitchen workflows, inventory management and delivery each ran on separate tools or manual coordination. The lack of integration produced frequent miscommunication between teams, delays in order processing and duplicated tasks across departments.
What we did
A microservices-based modular architecture where ordering, inventory, staff management, analytics and delivery each run as a self-contained service over one shared data layer, so the departments finally read from the same numbers.
0
2
Orders and stock handled on paper and unsynced POS
Order management and inventory tracking were largely manual, or handled through basic POS systems that never synced with stock levels. Paper tickets and disconnected tools invited human error, leaving inaccurate records, stock-outs of popular items and over-ordering of low-demand ingredients.
What we did
Real-time stock tracking with automatic alerts, plus AI-driven forecasting over historical orders, seasonal demand and menu performance that triggers supplier reorders before an ingredient runs out.
0
3
Scheduling run on spreadsheets and informal handoffs
Employee scheduling and coordination happened in spreadsheets or informally, with little automation and no real-time updates. Managers could not reliably track availability, absorb shift changes or cover peak hours, so services ran understaffed at the busiest moments and overstaffed at the quietest.
What we did
Staff management built as a first-class module with role-based dashboards designed per persona — owners, chefs, servers and delivery agents — and productivity insights surfaced alongside live service data.
0
4
Decisions made on assumption rather than data
Owners and managers had no centralised dashboard or real-time analytics. Live sales figures, peak service hours, customer preferences and menu performance were either unavailable or arrived too late to act on, so operational decisions rested on assumption.
What we did
Real-time analytics covering live sales tracking, peak-hour heatmaps, menu item performance and staff productivity, with automated scheduled reporting so stakeholders get the numbers without asking for them.
0
5
Every location effectively its own system
For groups running several sites, the absence of centralised management made scaling difficult. Each outlet kept its own processes for menus, pricing, staff and promotions, and the lack of standardisation showed up as inconsistent customer experience and diluted brand identity across locations.
What we did
A multi-tenant model with franchise and multi-location control: menus, pricing, promotions and settings pushed to every outlet at once or tuned per site, with benchmarking tools comparing branch performance.
Architecture
How it fits together
Simplified — the shape rather than every service.
0
1
CLIENTS
React.js web • React Native mobile • Kitchen display & POS UI
One React and React Native component library drives the web app, the mobile apps and the in-service screens, so a server on a handheld and a chef at a kitchen display are looking at the same system rendered for their role.
0
2
Gateway
Node.js APIs • WebSockets • Role-based authentication
Node.js services expose defined API contracts per module, with WebSocket channels carrying live order, kitchen and stock events, and role-based authentication deciding what each persona can reach.
0
3
Services
Ordering • Inventory • Staff management • Analytics • Delivery
Each core module is an independent service with its own API contract, so one can be updated, maintained or scaled without disturbing the others — and multiple teams could build them in parallel.
0
4
Data & infra
PostgreSQL • Redis • AWS • Docker • Kubernetes
PostgreSQL holds the operational record with indexing and query optimisation for high data volumes, Redis absorbs the hot reads, and the platform runs containerised on AWS under Kubernetes for peak-hour headroom.
Performance was an architectural requirement, not a later tuning pass: APIs are optimised to respond in under 200 milliseconds so that ordering, kitchen status and stock adjustments stay real-time through the busiest service of the day.
Solutions
Six systems doing the actual work
Not a features list — the specific things we built behind every number above.
Modular system architecture
Ordering, inventory, staff management, analytics and delivery each function as self-contained services, so updates, maintenance or scaling in one never take the others down with them.
Interfaces built for service pressure
High-contrast visuals, large touch-friendly elements and simplified navigation, designed to be usable on kitchen displays and handheld POS devices in a fast-paced environment.
Performance optimisation
APIs tuned to respond in under 200 milliseconds, with database indexing and query optimisation carrying large data volumes through peak business hours.
Integration & synchronisation
Native integrations with payment gateways, delivery platforms including Uber Eats and DoorDash, accounting software and supplier management tools, removing manual re-entry between systems.
Security & access control
Role-based authentication and permission systems restrict every user to the features and data their responsibilities require, reducing the risk of unauthorised actions.
Continuous testing & optimisation
Automated end-to-end testing pipelines run with every deployment, so functionality is verified against real restaurant scenarios before it reaches a live service floor.
Key features
What the platform does day to day
Five capabilities, each closing one of the gaps identified above.
| Capability | RUNS | Refresh | WHAT IT DOES |
|---|---|---|---|
| Live menu management | All channels | Instant | Update menus, specials, pricing and availability in real time across every ordering channel |
| Intelligent inventory management | Continuous | Real time | Live stock tracking with automatic alerts and reorder suggestions based on sales trends, to cut waste |
| Multi-location & franchise control | Group-wide | On publish | One dashboard for menus, pricing, promotions and performance across every branch |
| Real-time analytics & reporting | Owner & manager | Live · scheduled | Live sales dashboards, peak-hour insights, menu performance and staff productivity reports |
| Social engagement & loyalty | Diner-facing | On activity | Rewards, referral programmes, member discounts, dish liking and sharing to build community |
Integrations
How the moving parts plug in
Payments, delivery marketplaces, accounting and suppliers reach the platform through defined API contracts rather than sitting beside it as separate tools.
External systems
Payment gateways
Secure payments via Stripe
Delivery platforms
Uber Eats, DoorDash
Accounting software
No manual re-entry
Supplier management
Automated reorder triggers
↓
Through the platform layer ↓
Platform integration layer
Node.js API contracts
One contract per module
WebSocket data pipelines
Instant sync across devices
Role-based access
Owners, chefs, servers, agents
↓
into the core services ↓
Core services
Ordering
Inventory
Staff management
Analytics
Delivery
Because in-house POS, QR table ordering, the web app and third-party delivery all resolve against the same services, every incoming order arrives at one centralised kitchen display and is prioritised by preparation time and order type instead of by which system it came from.
Security
What protects operator and payment data
The platform holds sales records, payment flows, supplier terms and staff data across many tenants — so protection sits in every layer rather than at the edge.
Role-based authentication
Permission systems limit each user to the features and data their responsibilities require, so an owner, a chef, a server and a delivery agent each see a different platform.
Security in every layer
Data protection and controlled access were designed into the architecture from the start rather than added once the modules were live, reducing the risk of unauthorised actions.
Secure payment handling
Payments run through integrated gateway flows with Stripe, keeping card handling inside a dedicated path rather than spread across the ordering modules.
Reliable infrastructure
Containerised deployment on AWS with Docker and Kubernetes, plus automated end-to-end tests on every release, keeps service stable through peak hours across all tenants.
Process
How we got there
Five stages, starting on the service floor rather than in a feature list.
0
1
Research & discovery
On-site visits to quick service restaurants, fine dining venues and cloud kitchens, plus workshops with owners, kitchen managers and delivery coordinators, to observe real workflows and dependencies.
0
2
Wireframing & prototyping
Over 140 wireframes covering every major screen and journey, tested with real chefs, servers and managers so usability was validated before development rather than after it.
0
3
Module design
Ordering, inventory, staff management and analytics were each specified as independent modules with defined API contracts, letting several teams build in parallel without blocking each other.
0
4
Design system
A scalable design system built in Figma and translated into a reusable React and React Native component library — buttons, forms, typography, colour and layout patterns shared across every client.
0
5
Agile sprints & QA
Bi-weekly sprints delivered features incrementally, with each module tested against real-world restaurant scenarios for reliability and usability before release.
Business impact
What changed for the business
Beyond the headline numbers, three things operators noticed first.
A single source of truth
Operators gained something they had lacked entirely: one record of operations across floor, kitchen, stock and delivery, with owners getting live visibility instead of end-of-week guesses.
Real-time operations
New orders, kitchen status changes and inventory adjustments appear immediately on every connected device and location, which is what turned processing time into a measurable gain.
A revenue-driving platform
Managers automated routine tasks while waste fell and productivity rose, so the efficiency gains compound into a more profitable and more scalable business rather than a one-off saving.
Commercial outcome
What the engineering choices are worth in operating terms
Every headline number traces back to a specific decision, not a vague platform effect.
| Engineering decision | OPERATING OUTCOME | Measured effect |
|---|---|---|
| WebSocket pipelines & unified kitchen display | Orders from POS, QR tables, web and delivery arrive prioritised in one queue | 42% faster processing |
| AI-driven inventory forecasting & reorder triggers | Stock replenished against real demand instead of manual counts | 47% less food waste |
| Role-based dashboards & automated reporting | Staff stop reconciling spreadsheets and work from live task views | 26% higher productivity |
| Modular architecture with sub-200ms APIs | The platform holds up through peak service and scales outlet by outlet | 98% client satisfaction |
Stack
What it's built on
The actual technologies, not feature names with icons attached.
Frontend & mobile
- React.js
- React Native
- Figma
Backend
- Node.js
- WebSockets
- Supabase
Data & infra
- AWS
- PostgreSQL
- Redis
- Docker
- Kubernetes
Integrations
- Stripe
- Firebase
- Uber Eats
- DoorDash
Full case study
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