Encord

“"We went through a bunch of vendors and one of the things that stood out about Encord was the video first support, which other vendors do not have. Specifically understanding how the video works behind the scenes: the encoding, the frame indexes and square pixel ratios."”

““…as we started growing and adding more customers and more people using the tool there were certainly a bunch of challenges that came in, like our previous OS labeling tool kept running out of disk, so we had to start doing maintenance ourselves. We had to start editing the code and diverging from the main branch, which we really didn’t want to do…because we wanted to focus on our product.””

“The support has been phenomenal. Questions answered in about 20 minutes. And integrating with AWS just worked the first time I tried it.”

“For our AI initiatives, rapid iteration is critical. Encord and our ML infrastructure allow us to prototype learning tasks efficiently. The composability of Encord enables us to merge diverse data sources, facilitating extensive experimentation. With a well-integrated SDK, it's a matter of a few lines of code to achieve seamless integration and functionality.”

“Encord helps us to offer a ‘best in class’ capability for counting trees. It has helped us to improve the computer vision algorithm that allows us to delineate individual tree tops. [We were able] to count the entire forest in two minutes as opposed to two weeks.”

“By implementing Encord within our redesigned ML infrastructure, we've established an efficient end-to-end workflow from data sampling through to model training.”

“Encord integrates seamlessly into our entire AI infrastructure,”

““Encord’s team has been very hands-on in delivering what we need to succeed,” says Camilla. “A lot of companies promise that level of commitment, but Encord actually delivers it. We receive excellent support. They build out new features quickly based on our feedback. With other platforms, we’ve had to troubleshoot problems on our own– even to the extent that we basically needed to build our own offline tooling to process the data we labeled using them! Encord has worked with us to find a solution for every challenge.”

““Managing QA workflows efficiently and having an overview of the entire annotation process has been so important for our success. The more manual that QA process is, the more the amount of work explodes as the annotation workforce scales,” explains Camilla. “It’s always about balancing speed and quality. A lot of platforms prioritize speed over quality or quality over speed. Encord speeds up annotation while still allowing for strong quality control.””

““As our ambition grew, we realized that the functionality of many of the tools we were using was quite limited,” says Camilla Gilchrist, Head of Operations at Tractable. “We had a lot of steps in our annotation workflows, with many different pipelines, and the platforms couldn’t handle that complexity. We also had a bottleneck around quality assurance. With image segmentation, you can’t do a quick agreement rate analysis and doing a manual quality assurance check on each piece of data isn’t feasible. We needed a far more efficient way to assess quality.””

“It's a very easy platform to operate. What I really liked was the whole offering of Encord for both automated labeling and manual work, as precision is incredibly important to us.”

“With the model assistance, we found a much higher increase in efficiency within Encord simply because most labels were produced by a trained model and did not require correction.”

“Products like Encord are going to help us do that because they’re allowing us to unlock the potential of that data faster and in a more democratising way.”

“The interface is intuitive, so it requires very little training to become a sort of “expert” at using all the functionality.”

“Compared to other label assistance tools that we’ve worked with, Encord’s user experience is much smoother.”

“With Encord, we annotated the data over six times faster than using traditional methods.”

““Using Encord has more than halved the time it takes us to provide feedback on imagery. What could have previously taken us 2 hours now takes approximately 30 minutes.””

““The fact that Encord was able to offer both an automatic labeling tool as well as a team of annotators that we could use was very attractive. We need that flexibility at this stage in our journey,””

““The labeling speed of the annotation team improved to almost 60% compared to when using their in-house tool.””

““With other labeling tools, we needed to integrate another tool for data management and exploration capabilities, but Encord combined the two needs and provided a single comprehensive solution, along with excellent customer care and support,””

“With Encord, myself and other team members can label the data ourselves. We have numerous projects that we want to achieve this year, and I think having an easy to use platform where we can all work together is going to help us move them forward quickly and successfully,”

“We built separate pipelines for the three sets of images and labeled them accordingly. Then we trained and tested this small model. We got really good results, and it took more time to pull the images off the internet than it did to label them and build the model,”

“After they helped us put the images together, we pressed a couple of buttons, and the platform made a model for us,”

“We could also do computation both on the cloud and locally on our deep learning machine. All those factors made it really advantageous for us, and I saw the opportunity to save a lot of time and costs by using Encord to speed up our model development.”

“The platform was so intuitive. It provides a lot of different levels of control and team management. Everyone on the team could label and review images easily,”