Business Goals
Our client is a leading UK-based consultancy working in energy and utility management, responding to the changing needs of organisations across sectors from manufacturing to hospitality and retail to logistics. They have the expertise and insight to help successfully control costs, improve margins, achieve regulatory compliance and protect operational resilience. They help their customers make the right procurement decisions, better manage the complex process of utility billing, provide data and insight that improves operational and financial performance and provide expert consultancy that optimises consumption.
The client wanted to enhance its operations and with the sole purpose that its teams could efficiently service their customers in the least possible throughput time without inducing too much workload on the teams.
Technology Challenges
One of the critical services the client offers is Invoice Validation. Energy suppliers provide electronic and paper-form invoices that need to be validated in time for any mischarges and approved for timely payment. Data from these hardcopy invoices were manually extracted and entered into the system for processing. Current data processing methodologies led to human data entry errors and delays in delivery to their customers.
How NeosAlpha Helped
Our NeosAlpha’s technical experts leveraged the power of AI and machine learning to formulate a solution to address the challenges in manual processing of huge volumes of invoices.
The rich visual information from PDF files was extracted and categorised using Azure computer vision. After data validation, Azure Service Bus pushes the data concurrently to multiple robots for processing the data.
The process typically involves several steps. First, the invoices are scanned or uploaded into an Azure storage account. Next, Azure’s Computer Vision service is used to extract the relevant information from the invoices. This involves using OCR (optical character recognition) and other machine learning techniques to interpret the text and images on the invoices. The extracted data is then stored in a data store for further processing.
The solution was scalable to meet the sudden spike in the volume of invoices to be processed.
Results
Our solution helped the client with manifold benefits such as:
- The manual entry process of customer data is now completely automated, resulting in better accuracy and time to process
- This new process is time and cost-efficient compared to the manual process, improving end-client satisfaction
