I’m mapping the data-annotation vendor landscape for an upcoming study.
For many AI teams, outsourcing labeling is a strategic way to accelerate projects—but it isn’t friction-free.
If you’ve worked with an annotation provider, what specific problems surfaced? Hidden costs, accuracy drift, privacy hurdles, tooling gaps, slow iterations—anything that actually happened. Please add rough project scale or data type if you can.
Your firsthand stories will give a clearer picture of where the industry still needs work. Thanks!