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    Last Mile Delivery Platform Development
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    Last Mile Delivery Platform

    An UBER-like platform development for the fast delivery of goods.


    An Australian startup approached us to implement an UBER-like platform for the quick delivery of goods from the store to the consumer. Owners of ecommerce and offline stores could thereby save on the payment of couriers and drivers (they do not need to keep additional staff but only pay when the order is placed) and provide the best customer experience. For couriers, this means always having a job and thus income and flexibility in work when they are not tied to a fixed schedule, but can choose their own working hours. Besides, the solution must be secure in terms of data loss and environmentally friendly, since choosing the optimal route saves on overall fuel consumption.







    14 months




    Initially, the client wanted to transfer the Uber idea to the Logistics domain. On the technical side, competitors’ solutions were much simpler, as they did not guarantee same-day delivery and could take hours to process an order. But for us it was necessary to take into account at the same time a large number of stores and couriers who do not stand still and process a request in a couple of seconds.

    This platform is supposed to connect drivers and store owners. The client orders any product, the store specialist leaves a request for delivery, and the platform itself analyzes all available couriers, their location and offers the best delivery option, taking into account up to a kilometer of travel. The goal is same day delivery if the order is made from 8 am to 4 pm.

    Covering bigger markets/customers as the system grows can be challenging too. In the beginning the project was supposed to serve the P2P model, but the current goals are to connect with bigger entities. As well as providing next day delivery due to limitation of parcel serving from the customer side. It means the service will be working in both paces, same day delivery and as well next day delivery. 

    Another challenge is lack of information about the amount of parcels that will be created by system users and number of drivers being available. This task can be divided into 2 sub tasks. Either the number of drivers will be too big compared to the number of new parcels – so they could leave Deliver In Person infrastructure and move to competitors. Or the number of parcels will be too big compared to the number of drivers – then parcels won’t be delivered within the same day.

    UX/UI we made with our constant design partners – Zima design agency.


    As the main challenge was to work with a large amount of data and data streams in real time we solved the issue with fast-changing big data using machine learning and AI algorithms. To support long-term transactions, we chose Google Firestore. 

    For store owners, the platform is presented as a web application, for couriers it is a mobile version, since they are always on the go and must be mobile. The client leaves an application, fills in the pick up and drop off addresses, the size and quantity of parcels (up to 100x90x50 cm and up to 25 kg of weight) indicate specific data for the box if any and the system selects the most suitable carrier.

    The price is automatically calculated (at the moment there is only 1 price and a discount), the user sees the delivery status and gets a tracking number to follow it online.

    Serving bigger customers was resolved by architecture allowing for further system expansion so that the system will withstand any load that potentially appears in the future. As well any external component is thoroughly planned and tested before it will be used within the system in order to avoid potential issues.

    In order to minimize the issue of lack of drivers or lack of parcels, additional methods are utilized. Based on historical data, gig economy drivers receive information about potential increases in the number of parcels created by customers.

    In the first version of the solution, it works only in Sydney, in the next iteration, Melbourne, Brisbane and New Zealand are planned. Moreover, the goal is to reduce the delivery time to 3 hours.

    Technology Specification

    Cognitum Software House C#
    Cognitum Software House Javascript
    Cognitum Software House Python
    Cognitum Software House React
    Cognitum Software House Typescript
    Cognitum Software House Google Cloud Platform


    Xamarin.Native, C#, JavaScript, Geocoding, Places API


    Google Firebase, React, Typescript, JavaScript, Python, OR-tools, OSRM, Kubernetes, REST API


    Google Firestore, Google Cloud Storage, SFTP

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