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Using AI for 35x cheaper quality control

With Hasty, you can use AI to find potential errors and outliers for you and concentrate on fixing issues, not finding them

Why look for errors when you can fix them?

Today, almost all quality control and assurance is done completely manually. Whole teams sit and go through image by image to find potential issues, spending up to 30% of their effective time on this one task.

With our new error finder, you can use AI to find potential issues for you, and then accept and reject our suggestions. This approach has been shown to give you the same data quality 35x cheaper.

Better data for a fraction of the cost

By fixing instead of looking for errors, you can get incredible savings when doing QA without seeing any difference in quality of your data. After all, you are the one deciding what is an error and what isn’t - we only give you suggestions on what to fix.

State-of-the-art AI to find issues for any problem

With our Error Finder, you can find problems no matter what type of data you have. We can help you with finding wrongly classified bounding boxes, badly positioned polygons, and even artefact masks left from an erronous click.

Getting the full picture

We also give you a report on how many errrors we found, and if there are any particular patterns for what we see as potential issues. That approach gives you the full picture of how your previous annotation sprint went allowing you to quickly inform your team on where more attention is needed.

Getting started is easy

Sign up and try for free or book an onboarding call with one of our experts

Jenny Abrahamson

Software Engineer
at Audere

“Before discovering Hasty, labeling images was labor intensive, time-consuming, less accurate, and progression through the groundwork to build our AI detection model was much more frustrating. Hasty’s approach of training the model while labeling with faster annotate-test cycles has saved Audere countless hours. The speed and ease of use have allowed us to accelerate our mission to improve global health in the world’s most underserved communities.”