Blog
Notes from the Annotation Floor
Research we read, workflows we run, and the parts that break at scale. Written by the team that ships annotation data for enterprise AI.
Getting Better Training Data for Your AI Model: What Actually Works
Model performance lives or dies on data quality. The teams that ship reliable models follow the same four habits: clean datasets, clear instructions, calibrated annotators, and consensus where it matters.
What Document AI Research Tells Us About PDF Annotation Workflows
LayoutLMv3, Nougat, and TableFormer changed how researchers approach document understanding. We pull out the parts that matter when annotating real-world PDFs at production scale.
Why Robotics Datasets Need More Than Bounding Boxes
Bounding boxes annotate what is in a frame. Robotics policies need to know what is happening between frames. RT-1, BC-Z, and Open X-Embodiment all hinge on a more demanding kind of label.
How to Run a Five-Day Annotation Pilot That Actually Saves Time
Pilots either calibrate the project for production or burn a week and produce labels you throw away. The difference is mostly in the first 48 hours.
Have a data problem on your roadmap?
Tell us about the dataset and what you need it to do. We will respond with a scoping note and a sensible next step.