Changepond provided a path-breaking solution after making an in-depth analysis of the current process and inferring points to improve it. It used a two-step solution with an ML engine and an RPA bot. In the first step, the ML engine picks files from the shared folder, processes them, extracts data based on an inbuilt algorithm, and finally saves them in the shared folder in the Bot processable format. In the second step, the bot picks up the ML Engine-extracted data and prepares a memo file using generic templates. The prepared report is saved under the proper folder structure (/Processing, /Processed, /Not-processed) in the shared folder. The Scheduler/Trigger is used to start the ML-engine/RPA bot in the process.