Build an Automated Brand-Compliance Gate for AI-Generated Design Assets
Catch off-brand colors, banned imagery, and logo violations in generated assets automatically, before a human reviewer ever opens the file.
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This is a handoff plan, not an automatic integration. Review outputs before the next step. Unlabeled arrows show order, not a verified data format. Use the capability links to explore tool substitutions.
Step 1
Write Your Brand Rules as a Checklist, Not a Mood Board
Translate the brand guide into rules a model can actually verify: exact approved hex codes, minimum logo clear-space, banned imagery categories, required aspect ratios. Vague adjectives like "modern" or "clean" can't be scored automatically; specific, checkable rules can.
Tools
No catalog tool specified
Step 2
Generate Assets Through an API, Not a Manual UI
Call Fal.ai's developer API to generate the batch of assets programmatically, so every output can be piped straight into an automated check instead of a human opening each file individually.
Step 3
Score Every Asset with Claude's Vision API
Send each generated image to Claude's vision API alongside your structured rule checklist, and ask it to return a pass or fail per rule with the specific violation named, not just an overall impression.
Step 4
Auto-Regenerate Failures, Escalate Repeats to a Human
Feed any failed rule back into the generation prompt and try again automatically. If the same asset fails the same rule twice, stop regenerating and route it to a human instead of looping on a prompt that isn't going to fix itself.
Step 5
Know What This Gate Can't Catch
A vision model verifies objective rules like hex codes and banned imagery far more reliably than subjective brand feel. This gate catches technical compliance violations, not whether an asset actually feels on-brand, so keep a human in the loop for final creative judgment.
Tools
No catalog tool specified
Time Required
3-4 hours, one-time setup
Expected Result
A generation pipeline that automatically flags and regenerates off-brand assets against a checkable rule set, instead of catching violations by eye after the fact.
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