On-Demand Food & Quick Commerce
Personalised ordering that lifted repeat orders 35% — A full-stack AI food delivery app with a habit-based recommendation engine and dynamic delivery route assignment.
Order delays and a lack of personalised restaurant recommendations. Every user saw the same ranked list regardless of order history, and delivery assignment ignored kitchen preparation time — so riders queued at restaurants while food went cold elsewhere.
We engineered a full-stack AI food delivery mobile app in Python, implementing intelligent recommendation engines based on user ordering habits and dynamic delivery route assignment. Rider allocation is timed against predicted kitchen-ready time rather than order placement, and batching only groups orders whose routes genuinely overlap.
Streamlined logistics between restaurants, drivers and consumers, boosting repeat order rate by 35%.
Inside The Build
Six of the capabilities that made the difference, and why each one was there.
Ranking trained on each user's ordering history, time of day and repeat behaviour instead of a global popularity list.
Per-restaurant, per-dish preparation forecasting that drives when a rider is dispatched.
Live batching and sequencing across active riders, re-optimised as new orders land.
Restaurant-side console for menu, availability, prep-time tuning and settlement visibility.
Live map from kitchen acceptance to doorstep, with honest ETA updates rather than a fixed countdown.
Separate fulfilment path for grocery and convenience orders with different batching rules.
More Work
On-Demand Transportation & Mobility
AI dispatch that cut rider wait times by 40%
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Auto-routing citizen grievances to the right officer
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