From Midjourney to Production-Ready Assets

Concepts are step one. The leap from exploration to production is where many teams lose consistency. A clear last-mile pipeline turns promising images into reliable assets without losing the spark that made them compelling.

Start by deciding what kinds of assets you ship most and build a standard path for each. Social posts, landing hero images, app store screenshots, and ad units all have different requirements. For each, define the target dimensions, safe areas, type hierarchy, and export formats. The more you standardize the destination, the easier it is to guide generation toward what will actually ship.

Resolution and sharpness come next. Use a consistent upscaling method and be explicit about when to run it. If your process includes denoising or sharpening, set thresholds so images do not look over-processed. Logos and UI should almost always be handled as vector overlays rather than baked into generations. This keeps edges crisp and ensures brand marks render identically across assets.

Background cleanup is a common bottleneck. Decide whether backgrounds are real, synthetic, or flat fields, and set rules for how to replace or mask them. If you frequently replace backgrounds, create a small library of approved textures or environments to avoid one-off choices that introduce drift.

Type systems bring cohesion. Prebuild a set of layout templates that include headline, subhead, and call-to-action positions with the correct brand fonts and spacing. Dropping a generated image into a consistent frame does more for brand coherence than over-editing the image itself.

Finally, automate naming and exports. A predictable naming scheme improves search and reuse. Export presets for web, social, and ads reduce mistakes. When your last-mile pipeline is as intentional as your prompt system, your outputs stop feeling like experiments and start feeling like your brand.