Tender touch: Cube RM brings automation to trillion-dollar public procurement market

Tenders can serve as a lucrative inroad for new business, but the process of finding suitable opportunities and assessing a company’s chances of succeeding with their bid can be an arduous, resource intensive process — one that may ultimately end in failure. This is a problem that Cube RM wants to solve, with an AI-powered tender management platform that helps enterprises discover and win tenders globally.

Currently, a typical tendering process involves having to channel into local, country-specific data portals or signing up for email notifications, and then manually poring through each prospective tender. Built on top of the Salesforce platform, Cube RM integrates with myriad tendering portals across the globe, delivering new tender opportunities directly into the customer’s CRM as a qualified prospect.

“Cube RM combines global data and tender management functionalities into one single system that consolidates information and processes from all countries,” Cube RM cofounder and CEO Costas Economopoulos told VentureBeat.

Trillion-dollar opportunity

The public procurement market — that is, when governments and other public sector bodies buy goods and services form the private sector — was pegged as a $11 trillion industry in 2018, with some reports putting that figure at closer to $13 trillion today. That equates roughly to around 12 percent of global GDP, and highlights the size of the market that Cube RM is targeting.

Cube RM delivers predictive models trained using historical tenders, awards, and pricing data. This is gleaned and prioritized using natural language processing (NLP) techniques, and is combined with the company’s own internal marketing intelligence data.

“Our models take into consideration the specifics of each tender based on award criteria and other tender details, and accuracy is tested using cross-validation techniques,” Economopoulos said. “Even when historical data is absent or scarce, the predictive models can be trained with salespeople estimates, and provide initial insights which can improve later once additional data for training become available.”

This machine learning engine helps Cube RM users better evaluate and predict the likelihood of winning a contract, and even offers suggestions for the optimum bidding price. But perhaps more than that, Cube RM transforms unstructured data from past deals into a structured database of information relevant to a company’s market — this can be a useful perennial resource, even outside of tender processes.

“By extracting and systematically storing the information, it allows customers to save thousands of hours of manual work searching web sites and documents for information about the tender, but also maintain a database of structured information for their market and competition,” Economopoulos explained.

Other notable features embedded in the Cube RM platform include the ability to forecast when new tenders might be about to be published; facilitating the bid preparation and submission process; and tracking eventual tender winners for competition analysis.

Founded out of Athens, Greece, in 2018, Cube RM currently targets its wares at global pharmaceutical and medical equipment companies, with big-name customers such as Boston Scientific, Takeda, Kemira, and Bavarian Nordic on its books. But to expand into other markets such as manufacturing, the company today announced that it has raised $8 million in a series A round of funding from Runa Capital and Marathon Venture Capital.

“While working with global life sciences customers, we discovered that 25% to 60% of their revenues comes from public sector tenders, which is rather significant considering the trillion-dollar size of the market,” Economopoulos said. “Previously, there was no software system to discover new tenders that are being published every day; help those companies manage the processes for preparing their quotation; and find the best price to win the tender with the use of the latest AI technology.”