Expert Data Workflows for High-Stakes Industries

IngeData is a great, effective partner and I’m glad we decided to work with them. They helped us set up a scalable annotation process with a hybrid team of radiologists and technologists. What we previously handled in-house simply wasn’t scalable — IngeData made the process much easier.

IngeData's team of expert radiologists delivered precise, consistent annotations across more than 600 cases, with impressive efficiency. This project represents a major step forward in developing robust, clinically relevant AI models for breast cancer detection.

The collaboration with IngeData allowed XXII Group to reach a high level of attractivity towards data scientists, since we are now able to provide them with abundant annotated data. These annotations are delivered by IngeData through our dedicated team of 20 annotators working hand in hand with our team. Considering the very tight market conditions on the data scientists market, this is a considerable business result for us to grow fast.

IngeData brought great project management skills to our project. I was a bit worried about our tight deadlines, but the quality of the project architecture secured the annotation workflows and ensured swift deployment and delivery. We could use IngeData’s annotations to retrain our AI models and hit our model accuracy targets.

IngeData does a really good job. It’s great how we set a labelling process to segment and label our satellite images. They built a specialised team of annotators for photo interpretation and my data labelling pipeline is now more efficient and qualitative. It’s almost like having my own team of annotators. I just choose the image dataset and add it to Ingedata’s annotation backlog. Thanks, guys!

We have successfully partnered with Ingedata on multiple projects covering hundreds of CT scans. By setting up a team of radiologists with project managers in the long run, we have achieved very high quality and swift execution of the projects. This setup builds trust and alleviates a lot of the burden during training and ramp up, with constant feedback helping to improve the annotation set up. We have obtained consistent and clean results both in complex protocols and precise segmentations.