Machine Learning

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.

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Scaling for Growth

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.  

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Intelligent Data

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.

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Savings Realized

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.

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Fairmarkit delivers a simple sourcing platform to address the 20% of spend that’s not under management

See how Fairmarkit can put your data to work to receive an average of 6-12% cost savings, increase operational efficiencies by 30% and gain the ability to re-allocate your team to more strategic initiatives.
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