Groceries in ten minutes, run on software that never blinks.
A 10-20 minute promise leaves no room for wrong stock, a late rider or a mis-forecast dark store — and every error erodes margins that are already wafer-thin. CodeFacts builds the dark-store, dispatch and forecasting systems that keep the promise reliably while squeezing cost out of every order.
What's changing in quick commerce.
Quick commerce lives or dies on unit economics: sub-15-minute delivery, dense dark-store networks and rider fleets are expensive, and each cancelled order or idle rider destroys margin. The operators who win turn hyperlocal demand data, inventory accuracy and dispatch efficiency into a machine that hits the promise at the lowest possible cost per order.
What makes quick commerce hard.
The specific, structural challenges we build against — not generic pain points.
The delivery-time promise
Hitting 10-20 minutes end to end leaves no slack for picking delays, bad routing or rider shortfalls.
Dark-store inventory accuracy
Every phantom or missing SKU across dozens of micro-warehouses causes cancellations and refunds that kill trust.
Rider dispatch and routing
Assigning the right rider and route in real time across surging demand is a constant optimisation problem.
Hyperlocal demand forecasting
Demand shifts by neighbourhood, hour and weather, so store-level stocking must be predicted, not guessed.
Razor-thin margins
Small basket sizes and high delivery cost mean tiny operational inefficiencies wipe out profit per order.
Surge and peak load
Rain, weekends and campaigns spike orders simultaneously across a city, stressing every system at once.
From problem to platform.
Each challenge has an engineering answer. This is how we turn them into working systems.
Missed delivery windows
An orchestration engine that optimises pick, pack and dispatch as one flow, with live ETAs and proactive reassignment.
Inaccurate dark-store stock
Real-time inventory tracking with picker apps and reconciliation, so availability shown to shoppers matches the shelf.
Inefficient dispatch
Real-time rider allocation and route optimisation that batches orders and cuts idle and travel time.
Blind stocking decisions
ML demand forecasting at store, SKU and hour granularity that drives replenishment and reduces waste and stockouts.
Margin leakage
Operational analytics that expose cost per order across picking, delivery and refunds so teams optimise what matters.
Peak overload
Cloud-native, event-driven systems that auto-scale and shed load gracefully when a whole city surges at once.
Systems built for quick commerce.
Order orchestration engine
Real-time coordination of pick, pack, dispatch and delivery against the time promise.
Dark-store inventory system
Live stock accuracy with picker apps, reconciliation and availability that matches the shelf.
Dispatch and routing platform
Real-time rider allocation, batching and route optimisation across the fleet.
Demand forecasting
Hyperlocal ML forecasts by store, SKU and hour driving replenishment.
Customer and rider apps
Fast native apps for ordering, live tracking and rider operations.
Operations analytics
Cost-per-order and SLA dashboards that surface where margin leaks.
The service offerings we bring.
One partner across strategy, AI, engineering, cloud, data and growth — mapped to what this sector needs.
Outcomes we engineer for.
Faster, more reliable delivery times
Fewer cancellations from stock accuracy
Lower cost per order across the network
Systems that stay stable during surge
Questions we hear.
How do you help us hit the delivery-time promise consistently?+
By orchestrating picking, packing and dispatch as one optimised flow with real-time rider allocation and live ETAs, so bottlenecks are caught and reassigned before the window slips.
Can you improve dark-store inventory accuracy?+
Yes — with real-time tracking, picker apps and continuous reconciliation, so what a shopper sees as available genuinely matches the shelf and cancellations fall.
How does forecasting reduce our costs?+
ML forecasts demand at store, SKU and hour level, driving smarter replenishment that cuts both waste from overstock and lost sales from stockouts.
Will the systems hold up during surges?+
We build event-driven, cloud-native systems that auto-scale and degrade gracefully, so a rainy weekend across a whole city does not take you down.
Can you surface where our margin is leaking?+
Yes — operations analytics break down cost per order across picking, delivery, batching and refunds, so you can target the inefficiencies that actually move unit economics.