Food Delivery Platform

The Problem

Across FoodTech engagements, we kept solving the same problem for different clients: restaurant onboarding stuck in spreadsheets, dispatch logic living in a monolith that couldn't handle order spikes, and a consumer app that felt sluggish under real load. Every business rebuilding this stack from zero meant months of engineering spent on infrastructure that wasn't actually their differentiator — the differentiator was their restaurant network and their market, not their order-intake pipeline.

Specifically, three failure modes kept recurring in the systems we were called in to fix or replace: a single database transaction handling order writes, restaurant menu reads, and dispatch state all at once (meaning a menu update could slow down active order tracking); no queueing layer, so a lunch-hour order burst would time out requests instead of absorbing them; and restaurant onboarding that required a developer to manually seed data, which meant onboarding couldn't scale past a handful of restaurants a week.

System Architecture

We built the Food Delivery Platform as an event-driven system with three independently deployable services sitting behind a single API gateway, rather than one monolithic backend. The Order Service, Dispatch Service, and Restaurant Service each own their own responsibility and can be scaled, deployed, and debugged independently — an order-intake spike that hammers the Order Service doesn't degrade active delivery tracking running through the Dispatch Service.

Order intake writes go straight to PostgreSQL and simultaneously push onto a Redis queue that the Dispatch Service consumes — so a burst of incoming orders gets buffered and processed in order, rather than each request racing to write and match a driver synchronously. Live order-tracking state (where's my food) is cached in Redis rather than hitting Postgres on every poll from the consumer app, which is what keeps tracking responsive even during peak load. Restaurant onboarding runs through its own service with a self-serve flow, so new restaurants go live without a developer touching the database.

Technology Stack

• React Native — one codebase for the consumer app across iOS and Android, with offline-tolerant local state so the ordering flow survives a dropped connection mid-order
• Node.js — the same language across all three backend services, chosen for its strength at I/O-heavy, high-concurrency workloads like order intake and live dispatch matching
• Redis — order queueing to absorb bursts, plus caching for live tracking state so polling doesn't hammer the primary database
• PostgreSQL — the system of record for orders, restaurants, and users, chosen for strong transactional guarantees on order writes
• AWS — hosting and infrastructure, with auto-scaling on the Dispatch and Order services specifically, since those are the components that see the sharpest load spikes

Quantifiable Impact

Businesses running on the Food Delivery Platform get a fully working ordering and dispatch system from day one, with the flexibility to launch new markets without rebuilding core infrastructure each time. One business running on this platform scaled from a single pilot city to more than 40 markets within 12 months — growing order volume 3.2x while cutting average delivery time by 28%, and holding 99.95% uptime through repeated peak-hour surges, including three separate city launches in a single quarter.