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Case study

Trucker Assist App

The essence of the project is to provide a convenient service for truckers and truck owner operator clients. The project solves many pressing problems in the industry. The main advantage of the solution includes the ability to track loads from pick up to delivery to payment, easy submission of signed documentation at time of delivery, one-day payment to your bank account via direct deposit, management and execution of agreements with freight shippers/ brokers and procurement of rate confirmations, front and back hauls.

Challenge:

One of the main challenges was to implement a cost effective enhancement to improve the quality of overhead scans that a truck driver takes with his/her mobile device. Upon integrating with third party systems for payment processing it was found that often times the quality of the scanned documents did not meet the needs of these third party systems and could not be processed in a smooth and timely manner.

Solution:

Working closely with the client to research and test various options a solution was decided on using a combination of custom development and available libraries to enhance the scanning capabilities of the mobile app. The completed solution includes several image enhancing features including: document / edge detection, automatic cropping, perspective transformation, contrast adjustment and conversion to pure black and white.

Result:

The scanner enhancement has improved the overall process to a level that allows the client to manage these business processes seamlessly at a much more affordable cost than other existing analogues. Currently TAS is an actively developing business, with an actively growing user base, utilizing their proprietary custom software that is helping to streamline theirs and their client’s business processes.

Review:

“They brought an uncommon level of understanding to our project.”
Read 5 stars review on Clutch.co

Technologies

ASP.NET Core for Portal and mobile API
Xamarin for iOS and Android apps
– OpenCV for document recognition

Team

1 PM, 2 devs, 1 QA

Duration

8 months, ongoing

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