Batch processing

Batch refinement is for when you have dozens or thousands of assets and you need consistent improvements, fast.

The problem this solves

  • Manual editing doesn’t scale: per-asset tweaks are slow and inconsistent.
  • Quality drift: different people/edits create mismatched outputs.
  • Publish pressure: campaigns, catalogs, and libraries need quick turnaround.

Typical batch scenarios

Image

  • Product catalogs (PDP images), UGC cleanup, marketplace requirements
  • Screenshot libraries for docs and tutorials

Video

  • Weekly shorts with consistent output settings
  • Repurposing a library of older clips

Audio

  • Podcast back-catalog cleanup
  • Training library voice tracks from mixed recording environments

Text

  • Updating many pages to match a new tone/brand voice
  • Normalizing capitalization, terminology, and style across docs

Workflow (repeatable)

  1. Define the target: where will outputs be published (web, marketplace, social platform, podcast host)?
  2. Create a baseline preset:
    • Keep it conservative to avoid artifacts.
    • Prefer “slightly better everywhere” over “dramatic but risky.”
  3. Pick a representative sample (10–20 items):
    • Include best, average, and worst inputs.
  4. Run the sample batch and review:
    • Look for failure patterns (hair edges, fast motion, heavy reverb, ambiguous text).
  5. Adjust once and re-run the sample.
  6. Process the full batch.
  7. Spot-check outputs periodically (every N items) to catch drift early.

Output expectations

  • Faster delivery with consistent quality
  • Fewer manual touch-ups
  • More uniform “house style” across a library

Common pitfalls

  • One preset can’t fit everything: split into 2–3 presets if inputs vary widely.
  • Over-processing at scale: small artifacts become huge at volume.
  • No acceptance criteria: define what “good enough” means before you run the whole batch.

When not to batch

  • High-stakes assets requiring bespoke edits (hero images, flagship ads, legal statements).
  • Inputs are extremely heterogeneous (better to segment first).

Related pages