Shipamax raises $7 million to help improve logistics automation
Farringdon-based Shipamax, a startup that helps logistics companies automate their back-office action, plans to double in size after raising $7 million.
The company also plans to invest in both engineering development and customer success.
Mosaic Ventures led the round, with participation from Crane Venture Partners, Y Combinator, and other existing investors.
Founded in 2016, Shipamax initially intended to become an online broker for bulk shipping before transitioning to a SaaS offering for bulk shippers.
It then turned to develop a toolkit for back-office “process automation” for the global logistics industry after understanding the digitisation needs of the industry.
Shipamax co-founder Jenna Brown told TechCrunch: “It became clear that the underlying data extraction technology we’d built was driving the core value. After speaking to a number of people in adjacent segments of logistics, we saw companies there have the exact same problem – so it was clear we should really narrow the proposition down and widen out the segments we serve to the entire logistics market”.
The core Shipamax product connects to email inboxes or unstructured data sources and extracts data from emails and attachments in real-time, before producing a structured feed via the Shipamax API.
Logistics companies have previously tried to solve the digitisation problem using optical character recognition (OCR) tools such as Abbyy.
“The problem with OCR technology is that each company has to start from scratch – setting up hundreds of ‘templates’ to capture important fields and implementing ‘rules’ to interpret this data,” says Shipamax.
With the machine-learning in Shipamax's tech, the programme understands the context and there's no need to create templates or define rules within each organisation.
Shipamax processes over 18 million emails and documents per year and says that performance is tracked by analyzing orders, bills of lading, supplier invoices and other documents, with up to 99% accuracy for the most advanced types.