The Second Quality Layer That Catches What Automation Misses

Description and use

<p>Last March I had a batch of 40 vectors ready for Adobe Stock. The metadata CSV was generated by my pipeline, the uploads went out at 2 AM while I slept, and I woke up feeling like a genius. By noon, 11 of the 40 were rejected. Not for the CSV header, the header was right. For things the pipeline cannot see: a title that was technically accurate but read like a filename, keywords that were technically valid but described nothing in the image, and one file where the export had flattened a layer group in a way that left a visible seam. My automation had done its job perfectly. The job was just

Problem

<p>Last March I had a batch of 40 vectors ready for Adobe Stock. The metadata CSV was generated by my pipeline, the uploads went out at 2 AM while I slept, and I woke up feeling like a genius. By noon, 11 of the 40 were rejected. Not for the CSV header, the header was right. For things the pipeline cannot see: a title that was technically accurate but read like a filename, keywords that were technically valid but described nothing in the image, and one file where the export had flattened a layer group in a way that left a visible seam. My automation had done its job perfectly. The job was just

Outcome

<p>Last March I had a batch of 40 vectors ready for Adobe Stock. The metadata CSV was generated by my pipeline, the uploads went out at 2 AM while I slept, and I woke up feeling like a genius. By noon, 11 of the 40 were rejected. Not for the CSV header, the header was right. For things the pipeline cannot see: a title that was technically accurate but read like a filename, keywords that were technically valid but described nothing in the image, and one file where the export had flattened a layer group in a way that left a visible seam. My automation had done its job perfectly. The job was just

Evidence

The Second Quality Layer That Catches What Automation Misses

Source