Client:
Brown Strauss
Brown-Strauss Steel specializes in the distribution of steel products. Their comprehensive range includes wide flange beams, structural tubing, channels, angles, pipes, flats, universal mill plates, hot rolled strips, plates, and sheets.
Brown Strauss stands as the foremost structural steel service center across the United States. Their core expertise lies in handling extensive inventory and delivering large-scale projects. With nine strategically located facilities, Brown Strauss proudly offer next-day delivery services to the Midwest, the Rocky Mountain Region, the Southwest, the Pacific Northwest, and throughout California.
Our sales team was earlier using PDF to Excel conversion to upload data into our ERP system. We moved away from that process as the support was not great. Also, a single error would break the entire process.
The Challenge
Brown Strauss gets PDF files from customers. The sales team used to manually enter and upload data into the ERP. Typically, there are four fields: the item, the length, the quantity, and sometimes a piece mark to identify where that piece is going in the final product for the customer. The team was primarily using PDF to excel converters and spending considerable time formatting the excel results before uploading them into the ERP system SyteLine. So, a major part of the process was manual and time consuming.
Of all the solutions we tested, Nanonets did the best job of extracting information from our customer PDFs. Nanonets understood our requirements and provided a solution that integrates easily with our existing setup.
The Solution
Brown Strauss started evaluating document processing platforms. The company was looking to process over 50 thousand documents per year. As a result, they reached out to and tested over 7 to 8 products.
To each player they provided 4-5 documents to train the model on. During the demo call, they themselves tested the model with the 6th document. While many players did not return any results, Nanonets extracted data with over 95% accuracy. Each of the other OCR players were pushing to them more on their integration and export functionalities & lesser on their model accuracy. Brown Strauss just needed straightforward custom workflows. As a result, they found Nanonets economically feasible and best suited for their business needs.