The purpose of the Request for Quote (RFQ) is to leverage economies of scale to drive pricing down,
and reach the best value purchase. The historic balancing act has always been to determine how much
time should be spent sending out the requests. With Fairmarkit, you don’t have manage the trade-off
of your team’s time versus spend under management as you can easily automate the sourcing process.
There is a better way than sending your team through the manual task of searching Google, getting lost in
dozens of catalogs catalogs, scouring through spreadsheets, or more commonly just simply relying
on 1-2 or 2 tribal knowledge vendors. Fairmarkit uses machine learning to automate the
recommendation of vendors that should be included across 1,000s of individual purchases without human touch.
When starting off with a new enterprise customer we first apply the platform to make better use
of our customer’s existing vendor base. However, after partnering with them for a few months,
we use our recommendation system to invite outside trusted vendors to drive additional
value to the company. To continue supporting vendor consolidation initiatives, we also provide metadata on vendor performance to easily identify the bottom 5-10% of vendors that can be removed from your system.
The first step in getting control of your tail spend is to collect a steady stream of clean and
structured data. However, data alone doesn’t move the needle. Fairmarkit uses machine learning
techniques to generate actionable insight from your data that drives operational efficiencies and
flags potential areas of risk that require human interaction.