Food Delivery Platform Development

iOS and Android ordering apps and a responsive web ordering flow with restaurant discovery, menu browsing with allergen and dietary filters, basket management, and checkout. Allergen and dietary labelling follows EU FIR 1169/2011 CONTAINS and FREE_FROM classification conventions -- displaying the 14 major allergens per item and supporting filters for vegetarian, vegan, gluten-free, and other dietary requirements -- both to serve customers with dietary needs and to meet food labelling compliance obligations for operators selling in affected jurisdictions.

Real-time order tracking uses WebSocket push updates from confirmation through kitchen preparation to driver pickup and delivery. Order status changes (confirmed, preparing, ready for pickup, driver en route, delivered) push to the customer in real time with an ETA that recalculates based on actual driver position and traffic rather than the static estimate set at order placement.

Payment processing uses Stripe with fraud scoring -- Stripe Radar applies machine learning risk scoring to each payment, flagging high-risk transactions before they are authorised. This reduces chargebacks without requiring manual review of every transaction.

Push notifications at each order status change keep the customer informed without requiring them to keep the app open. Customer account stores order history, saved delivery addresses, payment methods, and dietary preferences. Reorder with one tap applies the previous order to the current restaurant menu, flagging items that are no longer available or have changed price before the customer confirms.

Restaurant-facing dashboard for order acceptance, prep time management, and kitchen status. Incoming orders are displayed with accept, reject, and delay controls -- a kitchen that is backed up can delay an incoming order rather than accepting it with an unrealistic prep time that creates a driver wait and a late delivery. Prep time adjustment in real time feeds directly into the dispatch system, which holds driver assignment until the pickup time calculation makes the dispatch viable.

Restaurant POS integration allows orders to flow directly into Toast, Square, or Oracle MICROS via webhook rather than requiring kitchen staff to manually re-enter the order into the POS from a tablet screen. The integration reduces kitchen errors from manual transcription and eliminates the latency between order placement and the kitchen receiving the ticket. POS integration scope depends on the restaurant partner's existing POS system and its webhook or API support.

Menu management covers item availability updates (86ing an item that is sold out pushes to the customer app immediately so customers cannot order unavailable items), price changes, new item creation, and modifier and option group management. All menu changes push live to the customer app within seconds, eliminating the version lag that causes customers to order items at stale prices or select unavailable modifiers.

For dark kitchen operations, food safety temperature monitoring integration logs holding temperature data for each kitchen station against HACCP compliance requirements -- maintaining the time-temperature records that food safety audits require and generating alerts when holding temperatures fall outside safe parameters.

Sales reporting shows revenue by day, week, and order type with the per-item and per-modifier data needed for menu engineering decisions.

Driver mobile app for order assignment, navigation, and status updates. Turn-by-turn navigation uses Google Maps or HERE Maps route optimisation -- selecting the fastest route given current traffic rather than the shortest distance, which matters most for delivery time accuracy in dense urban environments. Drivers receive a single interface showing their current assignment, the pickup address, the delivery address, and the running earnings for the shift.

Automated dispatch matches each confirmed order to the nearest available driver within the correct delivery zone using a scoring algorithm that combines proximity to the restaurant, estimated travel time factoring in current traffic, and restaurant prep time. The system dispatches the driver at the optimal moment -- when the estimated driver arrival coincides with the estimated order ready time -- so drivers do not idle at restaurants and food does not cool waiting for driver pickup.

Zone-based batching allows the dispatch system to assign a second nearby order to a driver who is already on a delivery, where the route geometry supports it and both delivery ETAs remain within acceptable bounds. This increases driver utilisation and reduces per-delivery cost without degrading customer experience.

For capacity overflow situations where driver supply is insufficient for demand, DoorDash Drive or Uber Eats integration provides access to gig driver capacity on a per-order basis, with overflow routing triggered automatically when in-house driver coverage falls below a configurable threshold.

Driver status management covers available, on delivery, break, and offline states. Order acceptance and rejection with automatic reassignment on timeout handles the case where a driver does not respond within the acceptance window.

Live order tracking map for the customer showing the driver's real-time location and the estimated delivery time. Driver position updates are transmitted from the driver app over WebSocket and pushed to the customer's tracking view in near real time -- the customer sees the driver moving on the map rather than a static pin placed at order assignment. Status updates at each stage -- order confirmed, kitchen preparing, driver assigned, driver en route, delivered -- are pushed to the customer's app and to the order history record simultaneously.

ETA prediction uses a gradient boosting model trained on historical delivery data: inputs include the distance between restaurant and delivery address, the current time of day (factoring in traffic density patterns by hour and day of week), the restaurant's historical average prep time for the current period, and real-time traffic data from the routing API. This produces a more accurate ETA than a simple distance-divided-by-average-speed calculation, reducing the gap between what the platform promises and what the driver delivers.

SMS fallback for customers who close the app sends a message at driver pickup and at 5 minutes before estimated delivery, keeping customers informed without requiring them to keep the tracking screen open. Push notification fallback covers the same status points for customers who have background notifications enabled.

For operators, the operations view shows all active orders on a map simultaneously -- each driver's position, their current assignment, and the ETA for each delivery in progress. This gives the operations team situational awareness across the delivery fleet in real time.

Delivery zone configuration by polygon, radius, or postcode defines the geographic boundaries within which each restaurant or dark kitchen will accept and fulfil orders. Per-zone delivery fee, minimum order value, and estimated delivery time baseline are configured separately for each zone -- an inner city zone may have a lower delivery fee and higher driver availability than an outer zone that requires longer drive times and commands a higher fee to remain margin-positive.

Dynamic pricing configuration activates a surge multiplier automatically based on configurable triggers: time of day (Friday evening dinner peak), weather conditions (rain events correlated with order spike), or driver-to-order ratio falling below a configurable threshold. Surge pricing is displayed transparently to the customer in the checkout flow, with the base fee and the surge amount shown separately so customers understand why the delivery fee is higher than the displayed zone baseline.

Zone availability management disables a delivery zone automatically when active driver count falls below the minimum coverage threshold for that zone, rather than accepting orders the platform cannot fulfil within the promised ETA. This is preferable to accepting orders and then delivering them late, which generates refund requests and rating damage.

Multi-restaurant marketplace architecture allows a single platform instance to serve multiple restaurant partners, each with their own menu, pricing, and delivery zone configuration, with the customer ordering from any restaurant in their delivery zone through a single app. Single-brand app architecture limits the platform to one restaurant group's outlets but allows tighter brand integration and a simpler customer experience. Both architectures are supported -- the choice is made at project scoping based on the operator's business model.

Operations reporting covers order volume, average delivery time from order confirmation to delivery, driver utilisation rate (percentage of on-shift time spent actively on delivery versus idle), and zone-level performance showing which zones are meeting ETA targets and which are consistently late. These metrics separate operational problems by cause -- a zone that is consistently late due to traffic requires a different response from a zone where the problem is driver under-coverage.

Late delivery tracking captures reason codes at the driver or kitchen level: kitchen delay (restaurant prepared the order late relative to the confirmed prep time), driver unavailability (dispatch could not assign a driver within the target time), traffic delay, or customer unavailability at the delivery address. Reason code data over a rolling period identifies which cause is responsible for the most late deliveries, directing improvement effort to the right place.

Customer satisfaction scores are linked at the order level to the restaurant, the driver, and the delivery zone -- so a pattern of low ratings associated with a specific restaurant (preparation quality problems) is distinguishable from a pattern associated with a specific driver (service quality problems) or a specific zone (delivery time problems).

Revenue reporting shows gross merchandise value, delivery fee revenue, and per-order margin by zone, restaurant, and time period. For marketplace platforms, restaurant partner performance is reportable in a way that feeds commercial conversations about minimum order values, delivery fee sharing, and exclusivity arrangements.

Driver performance metrics include acceptance rate, on-time delivery rate by zone, average delivery time, and earnings per hour -- the data that feeds driver incentive structures and identifies drivers whose performance metrics are below the fleet average.