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In current multi-enterprise supply chains, vendor collaboration goes far beyond just transaction processing and becomes a competitive advantage. However, many companies (especially smaller businesses) are still stuck with traditional approaches to vendor collaboration (e.g., email, spreadsheets, and phone calls). These methods result in delays, errors entering data, and difficulty tracking status, as well as interpersonal tensions among buyers and suppliers. This paper analyzes the design, build, and evidence of an online automated application platform that was specifically created to improve vendor collaboration. The platform provides for vendors' ability to onboard themselves, to manage the complete lifecycle of purchase orders (POs) automatically, to receive multi-channel notifications in real-time (e.g., email, SMS, in-app), to exchange documents centrally, and to view their performance through a dynamically updating vendor performance dashboard. The architecture of the application employs a cloud-native microservices model with RESTful APIs, role-based security (RBAC), and an asynchronous event-driven messaging model. Through a mixed-method research design that included a variety of pre- and post-implementation measures, vendor feedback via surveys, and analysis of system logs, the platform was evaluated during a 12-week trial using 12 vendors and 3 SME buyers. The evaluation identified three primary measures: order fulfillment times (OFT), communication error ratios (CER), and vendor satisfaction scores (VSS). Evaluating the trial data found statistically significant (p<0.01) reductions in OFT from a baseline 9.4 days to 5.5 days (41.5%), CER from 14.2%to 6.1 %( 57%), and an increase in VSS from 2.9 to 4.2 out of 5(44.8%). In addition to the quantitative improvements of the platform, qualitative comments indicated greater transparency, fewer followup emails from buyers to vendors, and faster dispute resolution. We will also discuss the challenges encountered in implementing the solution, including integrating with existing legacy systems, the initial resistance from users within the buying organizations, and governance of data security surrounding sensitive vendor data. Finally, we will outline future enhancements to the platform, including AI-driven predictive risk alerts, natural language processing (NLP)-based email-to-PO conversions, and potential functionality for supporting offline capabilities via mobile devices.