The idea for Arkestro arose out of a basic frustration with the amount of time and effort it takes most teams to collect and compare quotes from suppliers. Our founder Edmund was working as a procurement consultant and saw how much time his sourcing teams spent building Excel pivot tables. Regardless of the software, the problem was the same: a category expert was needed to identify potential errors in supplier submissions before an award scenario could be properly evaluated. There must be a better way, Edmund thought.
If any person can search and select hundreds of different items from different suppliers on a smartphone in under 5 minutes, then why should a similar process in a business context take so much longer and feel much more painful?
Edmund and Ben were friends growing up in DC. One evening in San Francisco’s Potrero Hill neighborhood, Edmund was describing the typical user experience on a modern procurement software application, and why the error-rate in supplier submissions stayed so persistently high. Ben, who had risen from the role of software engineer to lead an enterprise product at SurveyMonkey, suddenly had a lightbulb go off in his head. What if there were a way to automatically pre-populate the text in a sourcing project with little to no manual data entry required from procurement users or even from — gasp — the suppliers?
Using a technique known as “smart defaults” which Ben had leveraged at SurveyMonkey, the two began experimenting with recommendations in procurement cycles to improve data quality and increase completion speed.
That night, Arkestro (then called Bid Ops) was born. Using Edmund’s deep procurement domain expertise and Ben’s experience building user-centric software products, they began creating an app layered with pre-built intelligent recommendations for suppliers. This was where they found the opportunity to leverage machine learning, a set of algorithmic modeling practices within the broader field of artificial intelligence. After beginning to design the data model with Aziel Ferguson and Sam Griffiths, it became clear that the best way to generate and scale these intelligent recommendations was an AI engine leveraging decision trees and if-then logic to provide real-time error handling.
Edmund and Ben knew from experience that procurement teams did not want “another app”, and so they designed Arkestro’s model to run continuously inside of any existing customer system. This concept of “active monitoring” in Arkestro helps any procurement team dramatically improve cycle time, reduce time spent on manual data entry or analysis tasks and means with the exception of Admin-level users there is no login, no training and no requirement of user adoption in order to create business impact.
It was this focus on creating dramatic cost impact in under 90 days aligned with improved data quality and seamless no-login orchestration that got the attention of Gartner, Spend Matters and then Rob Desantis, an original co-founder of Ariba (now SAP Ariba). Rob saw in Arkestro an incredible synergy with the Procure-to-Pay platforms such as Ariba, Coupa, Zycus, Jaggaer and GEP with a dedicated focus on using real-time recommendations and predictive pricing to enable faster procurement cycles and thereby amplify the impact of procurement’s influence. After interviewing all Arkestro’s customers and employees, Rob decided to join as a co-founder and brought onboard the original Ariba Go To Market team. Rob, Ben and Edmund are now scaling Arkestro globally with a focus on enterprise procurement teams, and a world-class product and engineering organization focused on leveraging the power of real-time recommendations to drive exciting business outcomes.