Today, we are thrilled to publicly launch Cord and announce our $4.5M seed round led by CRV and including the Y Combinator Continuity Fund, WndrCo, Crane Venture Partners, Harvard Management Company, and Intercom.
When we first started Cord a little more than a year ago, we thought about how similar the state of AI was to the early days of computing and the internet. The potential of the technology was in full sight but the tools and processes surrounding it left much to be desired. Before starting the company, we experienced firsthand how the lack of tools to prepare quality training data was impeding the progress of building practical AI applications for everyone besides large tech companies. We wanted to de-FAANG AI by making access to quality training data available beyond Silicon Valley. With enterprises across every industry now evaluating AI as a core part of their business (over half deploying AI applications now compared to only 27% in 2019!), we think the time is right.
The challenge was to avoid being another data labelling service. Other platforms have done an excellent job making sure your data gets to the right outsourced provider and back to you with newly minted bounding boxes. But what if you cannot send your data externally, perhaps for privacy or security reasons? What if you need someone with special knowledge — say, a doctor — to label the data? (A pedestrian is easily recognizable; a polyp, not so much.) What if you cannot afford to pay an expensive expert to label video frames for 100s of hours so that you have sufficient data to train your model? Or an even simpler problem: what if you get back your labelled data from an outsourced workforce and realize you also need to label ‘table’ as well as ‘chairs’ — do you send it back and pay another fee? The only effective way to solve all these problems at once was automation. Our combined experience in research and building thousands of models was the foundation for developing a solution.
Cord is the only company that can automate labelling using our deeply technical approach, starting in computer vision. Our customers choose us because our novel algorithmic solution makes the annotation process orders of magnitude faster and cheaper, while allowing them to retain 100% control of their data.
Stanford Medicine’s Division of Nephrology chose Cord because we increase efficiency without sacrificing accuracy. In one lab, they conducted experiments five times faster while processing three times the number of images.
Another healthcare use case came out of our published research alongside King’s College London. We worked with collaborators Dr. Bu Hayee and Dr. Mehul Patel to annotate colonoscopy videos, which have relevant applications in cancer incidence. Since “colonoscopy” does not in itself inspire further reading, we’ll skip to the result: compared to open-sourcing labelling standard CVAT, Cord demonstrated an up to 16x increase in labelling efficiency, with our micro-models achieving > 97% accuracy for bounding box placement. An editorial in Endoscopy International Open cited our paper when discussing the potential of AI to reduce cancer-related mortality, reminding readers that “time is life!” By using Cord, doctors can focus their time on saving patients rather than drawing boxes.
Our team at Cord has been fortunate to work not only with healthcare innovators, but also with teams building applications in agriculture, autonomous vehicles, and retail. We are working at the vanguard of the latest AI technologies to facilitate the most efficient training data creation possible, regardless of the industry.
What is next? First and foremost, we are hiring. Take a look at our open roles here or email us at firstname.lastname@example.org. We have an ambitious vision and we need to build a world class team to take advantage of this enormous opportunity. We’re humbled to already work with some of the best, including lead investor Anna Khan, General Partner, at CRV and our core group of experienced, supportive investors.
And of course, if you are building AI applications, we would love to work together. Take a look at our article Label a Dataset with a Few Lines of Code to get a flavour for algorithmic labelling on real-world examples, and start using our platform today by booking a demo here.
Eric & Ulrik