DeepImage projects need more than a larger file. Strawberry can help organise image batches, compare requested outputs with source files, prepare naming and delivery checks, and coordinate approvals in the DeepImage tab before an asset reaches a campaign, store, or client.
01
Start with the asset that is actually approved.
A polished derivative is still wrong if it came from the wrong crop, revision, or brand file.
It can make the approved source and requested treatment visible before production starts.
02
Keep image batches from becoming a folder problem.
Production work slows down when nobody can tell which variants were requested, finished, or still waiting on feedback.
It can translate an unstructured folder into a batch the team can actually review.
03
Check the result against the use it will serve.
A marketplace image, a social crop, and a print asset can all need different framing and quality checks.
It can focus feedback on whether the output serves the destination, not whether it merely looks polished.
04
Give recurring asset work a consistent handoff.
A reusable skill can prepare the same intake and QA checklist whenever the next campaign batch opens.
It can carry a good creative handoff into the next cycle without copying yesterday’s mistakes.
Yes. It can examine the open project and prepare a review list for sources, requirements, outputs, and missing decisions.
No. It can surface the checks, but a human should make the final brand and quality call.
Yes. Turn a proven intake and QA process into a skill, then run it as a routine when new work arrives.
Open the batch in DeepImage and ask Strawberry to compare the visible sources, requirements, outputs, and missing decisions. It can prepare the QA pass, while your team makes the final brand and delivery call.
Yes. Once your team has agreed the checks, save that intake and QA method as a Strawberry skill, then run it when a new batch arrives.
DeepImage Strawberry