Leveraging machine learning techniques Fairmarkit intelligently understands the right set of vendors that should be added to each Request for Quote (RFQ) or Request for Service (RFS).The underlying technology is built to handle a significant amount of unstructured and incomplete data (tail spend data, tends to be this way by nature) and enable our customers to achieve procurement speed at an enterprise scale.
Fairmarkit understands which vendors sell items or services to different units of companies, across various categories. For Fairmarkit customers, scaling for growth doesn’t mean budgeting for a proportional increase in headcount or setting up outsourced bid desk teams.
Fairmarkit’s machine learning methods use our customers data to get more intelligent and efficient as it sources purchases through the platform. Initially, the platform relies on historical transaction data and buyer behaviors to auto-recommend vendors.
Over time, as the data continues to be collected and analyzed in a structured format, the system becomes more accurate and efficient for buyers, vendors and management reporting. Through Fairmarkit’s machine learning techniques, tail spend management clients are able to save an average of 7-10% on all spend run through the platform.