Apps that power on-demand delivery

Customer and driver applications, dispatch and tracking platforms, booking systems and operational dashboards, built for real time performance, reliability and scale. On demand delivery is technically demanding because three parties, the customer, your operations team and the driver, all need a consistent view of a job that changes continuously, and because demand arrives in sharp concentrated peaks. We design the real time layer and the peak capacity plan first, since retrofitting either into a system that assumed steady, low volume traffic amounts to rebuilding it.

Yes. We build route optimisation, arrival time prediction, demand forecasting and intelligent dispatch on current model stacks including Claude. The honest sequencing point is that if your dispatch is currently manual, a well designed rules based solver captures most of the available gain at lower cost than a learned model, and the machine learning becomes worthwhile once you have accumulated enough operational history for it to improve on that. We would rather propose it in that order than sell a model where a solver would serve you better.

Yes. We connect mapping, payment, messaging, point of sale and partner systems so bookings, locations and payouts stay in step in real time. Mapping costs deserve attention at design time rather than at the first invoice, because per request pricing on routing and geocoding can become a significant running cost at volume, and naive implementations that recalculate constantly are the usual cause. We design the call patterns and caching to keep that predictable, since an architecture that works fine in testing can be expensive once real order volume arrives.

Yes, and we design around it because on demand delivery load is extremely uneven. A large proportion of daily jobs can arrive in a short window, so a platform comfortable at average load can fail exactly when it is earning most. We build for high concurrency with queue backed dispatch, caching for read heavy tracking views, background processing for anything not needed immediately, and horizontally scalable infrastructure, then load test against realistic peaks. We also make sure the system degrades gracefully rather than failing completely under pressure.

You do, completely. You receive clean, documented, well structured code and full ownership of everything we build, held in your own repositories from the first commit rather than transferred at the end. There is no proprietary framework you can only maintain through us and no arrangement where you keep paying to use what you commissioned. For a platform business this becomes material at investment or acquisition, since the software is a substantial part of what is being valued and unclear ownership is an obstacle in diligence.

A booking site typically goes live in six to ten weeks. A full customer, driver and dispatch application suite takes twelve to twenty weeks depending on features and integrations, and we agree the timeline before starting. The driver application is routinely underestimated because it looks straightforward, but it carries the real time, background location and offline requirements that determine whether the whole operation is dependable. Native app store review is also outside anyone’s control, so we plan for it rather than treating submission as the finish line.

It depends on scope, how many applications are involved and which integrations you need. We scope to your goals and budget and send a fixed proposal, usually within 24 hours, so there are no surprises. For an operator starting out, a booking and dispatch platform with a driver application usually delivers the operational benefit at a fraction of a full multi sided marketplace. We would rather propose that first and let demonstrated volume justify the next phase than build capability that sits unused while capital is consumed.

Yes. Real time tracking and predicted arrival times are core to what we build, so customers can see progress and your team can see the whole fleet. Accuracy improves considerably once the platform has its own historical data, because generic mapping estimates do not know your pickup dwell times, local access difficulties or how your drivers actually work. We usually start with mapping provider estimates and refine using your own completed jobs, and we present a range rather than implying a precision the underlying data cannot support.

Through a driver lifecycle built into the platform: onboarding with document capture and verification, availability and shift management, job assignment and completion, and earnings with transparent payout records. Payout transparency is worth engineering properly because it is the single largest source of driver disputes and turnover: a driver who cannot reconcile what they were paid against the jobs they completed will not stay long. Battery and data usage on the driver application also deserve real attention, since constant location reporting done carelessly drains a phone within a shift.

We build service areas, distance and time based pricing, minimum fees and demand based adjustment as configurable rules rather than fixed logic, so your operations team can change them without a development cycle. On surge pricing specifically we would encourage transparency: showing an increased price clearly before a customer commits is legitimate, while obscuring it produces complaints, chargebacks and in some markets regulatory attention. We also make pricing changes auditable, so you can see what was in effect when a disputed job was priced.

The platform assumes it will happen, because connectivity fails in basements, lifts and rural areas routinely. The driver application queues status updates and proof of delivery locally and syncs when signal returns, so a completed job is never lost because of a dead zone. Operations sees the last known position clearly marked as stale rather than presented as current, and there is a manual path to reassign or complete a job. A system that requires connectivity to record completion will be worked around by drivers within days.

Yes. Every build includes thirty days of post launch support, and beyond that we continue with monitoring, security patching, updates and new features, and can provide a dedicated senior team that extends your capacity without hiring lag. The monitoring that matters here is operational rather than merely technical: dispatch queue depth, assignment latency and stale driver locations during peak periods tell you something is wrong long before an uptime check does, so those are what we alert on. We also review mapping and messaging costs periodically, because per request pricing scales with your order volume and is easy to leave unexamined once the platform is simply working.