We helped XPOfleet automate data mapping to unlock efficiency
We helped XPOfleet automate data mapping – cutting manual work and processing time.
About the client
XPOfleet is a leading provider of fleet and mobility management software solutions. They offer a unified platform that helps businesses integrate various mobility elements, such as vehicles, bicycles, fuel cards, charging cards, and mobility passes.
By providing valuable insights into total cost of ownership and sustainability metrics, XPOfleet empowers businesses to make informed, data-driven decisions, improve fleet performance, and enhance sustainability efforts. Their solutions have been widely adopted across Europe, with a focus on delivering innovative, cost-effective, and sustainable fleet management strategies.
The challenge: Overcoming the complexity of manual data mapping across multiple suppliers
XPOfleet collaborates with many suppliers, who, again, have numerous clients. They needed to upload various Excel files containing data that needed to be processed in a consistent format.
However, the format of these files varied from supplier to supplier, and even within the same supplier, files from different clients could have different data formats, such as varying languages, while still maintaining the same file structure.
XPOfleet wanted to develop an automated ETL solution capable of handling a range of source files, each containing data on vehicles, invoices, fuel, damages, or fines. Each file type already had its own target format, and the goal was to apply specific mapping rules to convert them into a standardised target file. That way, regardless of the file’s origin or structure, all data would be consistently mapped, processed, and saved in a standardised format tailored to each file type.
The solution: A custom ETL solution built from scratch
We built a custom ETL tool using Python and AWS, leveraging the client’s existing infrastructure. The tool runs as an AWS Lambda function, triggered automatically when a file is uploaded through the client's application, and from there to an S3 bucket.
The Lambda function handles everything. It retrieves the file, identifies the supplier and file type, applies the right mapping based on the documentation, transforms the data, and uploads the final CSV back to S3. Additionally, we implemented custom error handling, which allows the system to report an error if one occurs during data mapping.
If an error occurs, the system generates a JSON file detailing all the issues instead of the usual CSV file, and uploads it to S3. That way, the person downloading the uploaded file will see exactly where the error occurred, be able to fix the source file, and upload it again.
The code was organized in a way that each supplier and file type had their own classes which included importing and mapping logic, including all required transformations. This setup made the system easy to maintain, quick to extend for new formats, and clear to troubleshoot when needed.
The results: Transforming data workflows: faster, error-free, and scalable
We helped XPOfleet automate the entire data mapping process, remove manual work, and cut processing time.
That means suppliers can still use their existing file formats, and that is exactly when the magic happens. We ensured that every incoming file can now be automatically mapped into a single, consistent structure. No disruption, no rework.
The new system reduced operational risk, increased data reliability, and unlocked faster onboarding for new suppliers.
We helped the Client free up internal resources, allowing teams to shift focus from low-value tasks to strategic initiatives.
By designing a flexible, future-proof setup, we laid the foundation for long-term scalability and operational efficiency.
The tech stack:
- Python
- AWS Lambda
- AWS S3
- Serverless Framework
