Unit Economics Modelled From Sprint One
Cost per delivery and deliveries per courier hour are modelled during architecture, because batching and prep-time prediction decide profitability long before any customer-facing feature does.
We build customer, restaurant and courier apps as one system — with intelligent dispatch, prep-time prediction, live tracking and offline-first field operation engineered in from the first sprint.
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Food delivery platforms rarely fail on features. They fail on cost per delivery, on late orders during peak, and on merchants churning back to aggregators. Every build ships with these three layers working from day one.
Cost per delivery and deliveries per courier hour are modelled during architecture, because batching and prep-time prediction decide profitability long before any customer-facing feature does.
Dispatch and order intake are load-tested against a Friday-evening forecast, not an average afternoon that hides what happens when volume triples in twenty minutes.
POS and KDS integration means orders land in the system a restaurant already uses, instead of another tablet on the counter — usually the difference between a merchant staying and churning.
An order button is easy. What decides whether a food delivery business survives is the layer underneath it: whether two orders on the same street get batched, whether the courier arrives as the food is ready rather than ten minutes early, whether the app still works in a basement car park, and whether a delivery costs less than the fee you charged for it.
Online food delivery has settled into a permanent habit rather than a pandemic spike, and the competitive pressure has moved from acquisition to unit economics. The figures below frame the opportunity — replace each with a cited source before launch.
Placeholder metrics — replace with verified AptenTech data before launch.
Each service can be engaged on its own or as part of an end-to-end build.
Customer, restaurant and driver apps built as one system — ordering, dispatch, live tracking and payments — on an architecture that holds up when a Friday dinner rush triples order volume in twenty minutes.
Have a Food Delivery Idea? Let's Pressure-test It First.
Bring the business model, the launch city and the target date. You'll leave the call with a feature scope, a dispatch approach, a cost-per-order model and an honest view of what it takes to run.
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Each model below serves a different operator — marketplaces, restaurant chains, cloud kitchens, grocery and catering — and each one changes the dispatch logic, the compliance load and the revenue design.
Swipe or use the arrows. Client names and metrics are placeholders — replace with approved case studies.
See how businesses across different industries describe their experience working with AptenTech on their digital products and technology initiatives.
“AptenTech brought clarity to the entire development process. The team understood what we wanted, communicated consistently, and delivered a product that was both polished and practical to use.”
“The team was responsive whenever priorities changed and always explained the available options clearly. We appreciated their attention to detail and the smooth experience from development through launch.”
“AptenTech worked closely with our team instead of simply following a specification. Their suggestions helped improve several parts of the platform, and the final delivery matched our business requirements well.”
A working baseline for a production delivery platform. Everything is customisable — the list exists so scope conversations start from something concrete.
Everything the customer touches — from a hungry first search to a reorder they complete in under thirty seconds.
The stack below is what keeps a delivery business competitive three years after launch — dispatch that improves with every order, ETAs customers actually trust, and field software that works where there is no signal.
Assignment that weighs courier position, prep time, drop density and traffic together — the single biggest lever on cost per delivery in this category.
Per-restaurant, per-dish models that predict when food will actually be ready, so couriers stop idling at counters and quoted ETAs stop being optimistic fiction.
Predicting order volume by zone and hour so courier shifts are planned ahead rather than fixed with surge pricing after the backlog has already formed.
Home-feed and search ranking learned from ordering behaviour, time of day and basket history, surfacing the long tail instead of the same ten restaurants.
Distance, demand, weather and time-of-day pricing calculated per order, with floors that keep unprofitable deliveries off the board entirely.
Pattern analysis across refund claims, promo usage, courier behaviour and payment signals — refund abuse is a real margin leak at scale in this category.
Automated menu structuring, dish tagging and image handling at ingestion, so onboarding a restaurant takes minutes rather than a manual data-entry session.
High-frequency location streaming with map-matching and battery-efficient background updates, so tracking stays accurate without draining courier phones.
Couriers work in basements, lifts and dead zones. Orders, status updates and proof-of-delivery photos are captured locally and reconciled when signal returns.
Want Intelligent Dispatch and Prep-time Prediction in Your Platform?
We'll show you what is realistic at your order volume, what dispatch and tracking actually cost to run, and which parts are worth building before launch rather than after.
Discuss Your AI RequirementsFood delivery touches food safety, payments, gig-worker rules and age-gated goods all at once. These standards are scoped at design time and enforced in the software, not handled on paper after the first complaint.
Six connected stages. Select any stage to see what it produces.
Stage 01 of 06
Business model, city strategy, delivery model and commission structure. We model cost per order alongside the feature scope, because in this category the two decide each other.
Cost is driven by how many apps you need and how intelligent the dispatch has to be — not by a price list. The tiers below show what changes as the platform grows — replace each range with your verified pricing before launch.
| Platform tier | What it includes | Typical timeline | Investment |
|---|---|---|---|
| MVP | Customer app with ordering, a restaurant console, a courier app with basic assignment, payments and one city. | 14–20 weeks | Available on request |
| Growth | Everything in MVP plus batching and prep-time prediction, POS integration, loyalty and promotions, and a full operations console. | 6–10 months | Available on request |
| Scale | Multi-city operations, demand forecasting and courier supply planning, aggregator integration, grocery or age-gated categories and white-label tenancy. | 10–15 months | Available on request |
Chosen for field reliability, dispatch performance and the ability to staff every layer twice over.
Plenty of teams can build an order screen. The difference shows up in the parts customers never see — the dispatch engine, the prep-time model, the courier app used one-handed on a scooter, and the unit economics that decide whether growth makes you money or costs you money.
Cost per delivery, deliveries per courier hour and contribution margin are modelled during architecture, so the business case is tested before the build rather than after the pilot.
Batching, prep-time prediction and routing are where this category is won or lost. We build them properly instead of shipping naive nearest-courier assignment and hoping.
Customer, restaurant and courier apps share one order state machine, so a status never means two different things in two places.
Load testing runs against a Friday-night forecast rather than a typical afternoon, because that is the hour that decides your reputation.
Code, infrastructure, design files and documentation are yours from day one, with a handover that leaves your team self-sufficient.
Strategy, design and engineering in one place — no gap between vendors where responsibility quietly disappears.
The questions founders and operators actually ask on the first call — cost, dispatch, unit economics, POS integration and who owns what at the end of it.
It depends on how many apps you need — customer, restaurant and courier is three products, not one — plus whether you build your own dispatch engine or start with simpler assignment. A focused single-city MVP sits at the lower end; a multi-city marketplace with batching, POS integrations and a full ops console sits considerably higher. We break every estimate down by workstream.
A scoped MVP covering customer, restaurant and courier typically runs fourteen to twenty weeks. Adding intelligent dispatch, POS integrations, loyalty and multi-city operations usually takes it to six to ten months. We give a range with the assumptions written down rather than a single optimistic number.
No. Many operators launch by integrating third-party delivery providers, or by letting restaurants deliver themselves, and only add an owned fleet once order density in a zone justifies it. The platform should support all three models without a rebuild.
Drop density and batching. Two orders on the same street cost far less to serve than two across a city, and every minute a courier idles at a counter is margin gone. That is why prep-time prediction and batching matter more than almost any customer-facing feature.
Yes. POS and KDS integration is what stops restaurants running your tablet as a separate island — orders flow into the system they already use, which is usually the difference between a merchant staying and churning.
Load testing against a realistic Friday-evening forecast rather than an average day, with auto-scaling, queue-based order intake and dispatch that degrades gracefully instead of collapsing when volume spikes.
The courier app is offline-first. Order details are cached ahead of the route, and status updates, photos and signatures are captured locally then reconciled when signal returns. Basements, lifts and multi-storey car parks are normal conditions, not edge cases.
Most operators combine two or three: commission from restaurants, a delivery fee from customers, and either subscriptions for free delivery or advertising placement for merchants. We model each against your cost per order so the mix is chosen on economics rather than preference.
Yes. Consolidating orders from multiple aggregators into one merchant screen, with one menu synced everywhere, is a distinct product and often an easier commercial entry point than competing with the aggregators directly.
Refund abuse is a genuine margin leak at scale. We build pattern detection across refund claims, promo usage and account behaviour, plus dispute workflows that give your support team evidence rather than guesswork.
You do. Repositories, infrastructure, design files and documentation are yours and handed over as part of every engagement. We sign an NDA before you share anything sensitive.
Often, yes. Many engagements involve replacing the dispatch engine, adding POS integrations or rebuilding the courier app while keeping the rest intact. We start with a review and tell you honestly whether a rebuild is genuinely cheaper than a rework.
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