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Trackhive

We used AI & ML to build a robust shipment tracking system

Trackhive is already a very integrated system for tracking, which integrates with over 550+ retail platforms like shopify, amazon, and more. With the help of AI and ML, users can now accurately get predictions on the estimated delivery date.

PLATFORMS
TECHNOLOGY

Core java,
React Native

LOCATION

U.S.A

What We Achieved

An AI & ML driven tracking system that seamlessly integrates with over 500 online retail suppliers.

We used AI & ML to build a robust  shipment tracking system
Challenges

Mobile App Planning and Development

Data quality and Collection

Data quality and Collection:

Training an AI requires an extensive data set so that it can accurately and intelligently take understand the patterns and predict situations. The team had to first compare the data models and listed the types and categories of data available. It is very important to know the type of data you have available at hand and what data is actually required for analysis.
Data Labelling and categorization

Data Labelling and categorization:

Most of the systems that utilize machine learning have to be trained by specifically labelling the available data. Choosing the perfect approach for data labelling is an integral challenge to AI and ML. Choosing the wrong approach could drastically increase the cost of labelling, and also the time is taken to analyse the data.
Explainability

Explainability:

AI & ML are used to reach conclusions that would rather take a lot of human time and effort. It is important that users of the data can easily figure out how the effort was made and what could be done to improve the decision making progress. Decisions themselves are often now enough. It is also a requirement of the user to know how the decision was reached.
Case Specific Learning

Case Specific Learning:

While most intelligent systems simply rely on the patterns, it is also important that the system understands case-specific requirements. The transfer of learning should happen just like humans do through interaction. It was important to find other AI systems that have similar models and operate in a similar fashion so that better decisions can be reached using co-learning.
SOLUTIONS

Building the App

Deep understanding of Data accumulation

Deep understanding of Data accumulation:

Our team worked throughout the project, starting with a deep understanding of what type of data was required. For example, data was collected from multiple channels, like shipment tracking bots, courier services, GPS location of the vehicle, roadblocks, delays due to weather conditions and much more to reach a conclusive required data set.
Systematic Data Labeling

Systematic Data Labeling:

The team was clear about cost reduction and progress tracking, which is why, the complete labeling system was developed in house and each and every aspect of data labeling that can be useful for the system to understand and predict was studied and analyzed, because of which the accuracy of labeled data was increased, and the process remained in total control of our clients, without the possibility of data manipulation or security.
Case Specific Learning

Case Specific Learning:

Since our AI and ML was deeply integrated with over 500+ retail systems, it anonymously learns from the shipping details provided by them and predicts their shipping speed and lags to understand the tracking. This can help the customer get the almost perfect data on when their shipment can be tracked and how long it would take, accurately and precisely.